Introduction

Our research focuses on performing Exploratory Data Analysis (EDA) on Google Play Store apps to uncover patterns, trends, and insights regarding app characteristics, user behavior, and installation patterns. We are trying to see how app popularity, defined as the number of installs with high reviews and ratings, is impacted by categories, last updated, app sizes, version, and other factors.

Smart Question

“What is the impact of content rating, required Android version, app category, size, and pricing on predicting app success in terms of positive ratings and high user reviews, as well as the number of installs, using data from Google Play Store apps from 2010 to 2018?”

Specific: The question clearly defines the variables (content rating, required Android version, app category, size, pricing) and the outcomes (positive ratings, high user reviews, number of installs).

Measurable: The outcomes (positive ratings, high user reviews, number of installs) are quantifiable.

Achievable: Given the availability of Google Play Store data from 2010 to 2018, the analysis is feasible.

Relevant: The question addresses a significant issue in the app development and marketing industry: predicting app success.

Time-specific: The timeframe (2010-2018) is clearly defined.

Data Preparation and Cleaning

Here, we have loaded the dataset ‘Google Play Store Apps’ stored in csv file using ()

#Loading the Dataset
data_apps <- data.frame(read.csv("googleplaystore.csv"))
#Checking the structure of the data
str(data_apps)
## 'data.frame':    10841 obs. of  13 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : chr  "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : chr  "159" "967" "87510" "215644" ...
##  $ Size          : chr  "19M" "14M" "8.7M" "25M" ...
##  $ Installs      : chr  "10,000+" "500,000+" "5,000,000+" "50,000,000+" ...
##  $ Type          : chr  "Free" "Free" "Free" "Free" ...
##  $ Price         : chr  "0" "0" "0" "0" ...
##  $ Content.Rating: chr  "Everyone" "Everyone" "Everyone" "Teen" ...
##  $ Genres        : chr  "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
##  $ Last.Updated  : chr  "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
##  $ Current.Ver   : chr  "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
##  $ Android.Ver   : chr  "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...
#First 5 rows of the dataset
head(data_apps)
##                                                  App       Category Rating
## 1     Photo Editor & Candy Camera & Grid & ScrapBook ART_AND_DESIGN    4.1
## 2                                Coloring book moana ART_AND_DESIGN    3.9
## 3 U Launcher Lite – FREE Live Cool Themes, Hide Apps ART_AND_DESIGN    4.7
## 4                              Sketch - Draw & Paint ART_AND_DESIGN    4.5
## 5              Pixel Draw - Number Art Coloring Book ART_AND_DESIGN    4.3
## 6                         Paper flowers instructions ART_AND_DESIGN    4.4
##   Reviews Size    Installs Type Price Content.Rating                    Genres
## 1     159  19M     10,000+ Free     0       Everyone              Art & Design
## 2     967  14M    500,000+ Free     0       Everyone Art & Design;Pretend Play
## 3   87510 8.7M  5,000,000+ Free     0       Everyone              Art & Design
## 4  215644  25M 50,000,000+ Free     0           Teen              Art & Design
## 5     967 2.8M    100,000+ Free     0       Everyone   Art & Design;Creativity
## 6     167 5.6M     50,000+ Free     0       Everyone              Art & Design
##       Last.Updated        Current.Ver  Android.Ver
## 1  January 7, 2018              1.0.0 4.0.3 and up
## 2 January 15, 2018              2.0.0 4.0.3 and up
## 3   August 1, 2018              1.2.4 4.0.3 and up
## 4     June 8, 2018 Varies with device   4.2 and up
## 5    June 20, 2018                1.1   4.4 and up
## 6   March 26, 2017                1.0   2.3 and up

Description of the App Dataset Columns

  1. App: The name of the application, represented as a character string.
  2. Category: The main category of the app, such as “ART_AND_DESIGN,” represented as a character string.
  3. Rating: The average user rating of the app, recorded as a numeric value.
  4. Reviews: The total number of user reviews for the app, shown as a character string.
  5. Size: The size of the application, represented as a character string.
  6. Installs: The approximate number of installations for the app, stored as a character string.
  7. Type: Indicates whether the app is free or paid, represented as a character string.
  8. Price: The price of the app, stored as a character string. Free apps are listed as “0,” while paid apps have a dollar amount.
  9. Content.Rating: The target age group for the app, represented as a character string.
  10. Genres: The genre(s) of the app.
  11. Last.Updated: The date of the app’s last update, stored as a character string.
  12. Current.Ver: The current version of the app, represented as a character string.
  13. Android.Ver: The minimum Android version required to run the app, stored as a character string.

Apps

# Checking the type of the App 
typeof(data_apps$App)
## [1] "character"

Checking for duplicated apps and removing

#Display all the duplicated Apps
duplicate_apps <- aggregate(App ~ ., data = data_apps, FUN = length)  
duplicate_apps <- duplicate_apps[duplicate_apps$App > 1, ] 
duplicate_apps <- duplicate_apps[order(-duplicate_apps$App), ] 

#View(duplicate_apps)
#print(duplicate_apps)

print(paste("Number of duplicated Apps:",nrow(duplicate_apps)))
## [1] "Number of duplicated Apps: 404"
#Removing Na values and duplicates
data_clean <- data_apps[!is.na(data_apps$App), ] 
data_clean <- data_clean[!duplicated(data_clean$App), ] 

#(After removing the duplicates) Unique values
unique_apps <- length(unique(data_clean$App))
print(paste("Number of unique apps after removing the duplicates:", unique_apps))
## [1] "Number of unique apps after removing the duplicates: 9660"

Duplicate App Analysis:

  • 404 apps were repeated either twice or thrice.
  • After removing duplicates, the dataset now contains 9660 unique apps.
  • Total duplicates removed: 1181 apps.

After dropping duplicate

str(data_clean$App)
##  chr [1:9660] "Photo Editor & Candy Camera & Grid & ScrapBook" ...

Price

typeof(data_apps$Price)
## [1] "character"

Convertion of Price to numerical

There is ‘$’ present after each price of the App. Check and remove before conversion.

#To check if there is dollar symbol present 
#data_clean$Price[]
# Remove dollar symbols and convert to numeric
data_clean$Price <- as.numeric(gsub("\\$", "", data_clean$Price))
#Recheck for dollar symbol
#data_clean$Price[]

All the dollar symbols are removed succesfully.

# Summary statistics for price
summary(data_clean$Price)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   1.099   0.000 400.000       1

From the unique_df, there is a missing value present in the Price column. Let’s handle it!

Checking for missing values in Price

missing_na <- is.na(data_clean$Price)    
missing_blank <- data_clean$Price == "" 

sum(missing_na)
## [1] 1
sum(missing_blank, na.rm = TRUE)
## [1] 0
# Remove row where Price is NA or blank
data_clean <- data_clean[!is.na(data_clean$Price) & data_clean$Price != "", ]

Have removed one row #10473 which app does not have a category nameas it is not relevant to our analysis.

#Recheck for missing values
summary(data_clean$Price)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   0.000   0.000   1.099   0.000 400.000
Missing values removed succesfully. (Price)

Type

#Checking the type of Type variable
table(data_clean$Type)
## 
## Free Paid 
## 8902  756

From the price column, we can see 8903 apps are free but it is misread somewhere in the Type column. So lets check!

#Checking for Missing values
print(paste("Missing values:",sum(is.na(data_clean$Type))))
## [1] "Missing values: 0"
data_clean[is.na(data_clean$Type), ]
##  [1] App            Category       Rating         Reviews        Size          
##  [6] Installs       Type           Price          Content.Rating Genres        
## [11] Last.Updated   Current.Ver    Android.Ver   
## <0 rows> (or 0-length row.names)
# Replace NaN or missing values in the Type column with "Free"
data_clean$Type[is.na(data_clean$Type)] <- "Free"

There is one row 9150, has a missing value for Type. As the price is 0, replaced it with “Free”.

Missing values handles succesfully. (Type)

Size

# Checking the type of the Size 
typeof(data_apps$Size)
## [1] "character"

Cleaning size Variable

# Replace "Varies with Device" in the Size column with NA
data_clean$Size[data_clean$Size == "Varies with device"] <- NA
data_clean <- data_clean[!grepl("\\+", data_clean$Size), ]
data_clean$Size <- ifelse(grepl("k", data_clean$Size),
                          as.numeric(gsub("k", "", data_clean$Size)) *
0.001,  # Convert "K" to MB
                          as.numeric(gsub("M", "", data_clean$Size)))
# Remove "M" for megabytes

Replacing missing Size values with mean value

# Calculate and display the mean size for each category in the 'Type' column
mean_size_by_type <- tapply(data_clean$Size, data_clean$Category,
mean, na.rm = TRUE)
print(mean_size_by_type)
##      ART_AND_DESIGN   AUTO_AND_VEHICLES              BEAUTY BOOKS_AND_REFERENCE 
##           12.370968           20.037147           13.795745           13.134701 
##            BUSINESS              COMICS       COMMUNICATION              DATING 
##           13.867194           13.794959           11.307430           15.661119 
##           EDUCATION       ENTERTAINMENT              EVENTS              FAMILY 
##           19.057101           23.043750           13.963754           27.187988 
##             FINANCE      FOOD_AND_DRINK                GAME  HEALTH_AND_FITNESS 
##           17.368127           20.494318           41.866609           20.669707 
##      HOUSE_AND_HOME  LIBRARIES_AND_DEMO           LIFESTYLE MAPS_AND_NAVIGATION 
##           15.970258           10.602883           14.844916           16.368121 
##             MEDICAL  NEWS_AND_MAGAZINES           PARENTING     PERSONALIZATION 
##           19.189399           12.470189           22.512963           11.224624 
##         PHOTOGRAPHY        PRODUCTIVITY            SHOPPING              SOCIAL 
##           15.666158           12.342505           15.491435           15.984090 
##              SPORTS               TOOLS    TRAVEL_AND_LOCAL       VIDEO_PLAYERS 
##           24.058361            8.782837           24.204410           15.792756 
##             WEATHER 
##           12.680036
# Loop through each row and replace NA values in the Size column with the mean size of the corresponding category
data_clean$Size <- ifelse(
  is.na(data_clean$Size),  # Check if Size is NA
  round(mean_size_by_type[data_clean$Category], 1),  # Replace with the mean size based on the Category
  data_clean$Size  # Keep the original size if it's not NA
)

summary(data_clean$Size)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   0.0085   5.3000  13.1000  20.1512  27.0000 100.0000

Installs

####Remove the ‘+’ sign, Remove the commas, Convert to numeric

#clean installations
clean_installs <- function(Installs) {
  Installs <- gsub("\\+", "", Installs)  
  Installs <- gsub(",", "", Installs)    
  return(as.numeric(Installs))           
}

data_clean$Installs <- sapply(data_clean$Installs, clean_installs)

nan_rows <- sapply(data_clean[, c("Size", "Installs")], function(x) any(is.nan(x)))

# Display only rows that contain NaN in either Size or Installs
data_clean[,nan_rows]
## data frame with 0 columns and 9659 rows
datatable((data_clean), options = list(scrollX = TRUE ))

Display the unique values

# Identify the unique values in the 'Installs' column
unique_values <- unique(data_clean$Installs)

# Display the unique values
print(unique_values)
##  [1] 1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 1e+06 1e+07 5e+03 1e+08 1e+09 1e+03
## [13] 5e+08 5e+01 1e+02 5e+02 1e+01 1e+00 5e+00 0e+00
# Function to convert the installs to numeric
convert_to_numeric <- function(x) {
  # Remove non-numeric characters and convert to numeric
  as.numeric(gsub("[^0-9]", "", x)) * 10^(length(gregexpr(",", x)[[1]]) - 1)
}

# Sort unique values based on the custom numeric conversion
sorted_values <- unique_values[order(sapply(unique_values, convert_to_numeric))]

# Convert sorted values to character without scientific notation
formatted_values <- format(sorted_values, scientific = FALSE, trim = TRUE)

# Update the original 'Installs' column in data_clean based on the numeric conversion
data_clean$Installs <- format(data_clean$Installs, scientific = FALSE, trim = TRUE)

data_clean$Installs <- as.numeric(data_clean$Installs)
# Display summary of the updated 'Installs' column
summary(data_clean$Installs)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.000e+00 1.000e+03 1.000e+05 7.778e+06 1.000e+06 1.000e+09

Rating and Reviews

# Checking the type of the Rating 
typeof(data_clean$Rating)
## [1] "double"
# Checking the type of the Reviews 
typeof(data_clean$Reviews)
## [1] "character"

Checking the format of Rating and Reviews

##  chr [1:9659] "159" "967" "87510" "215644" "967" "167" "178" "36815" ...
##  num [1:9659] 4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...

As we can see the Review column is in string format which could be converted into int for more insights.

Change the column reviews from Str to int

## 'data.frame':    9659 obs. of  13 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : chr  "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : num  159 967 87510 215644 967 ...
##  $ Size          : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs      : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Type          : chr  "Free" "Free" "Free" "Free" ...
##  $ Price         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Content.Rating: chr  "Everyone" "Everyone" "Everyone" "Teen" ...
##  $ Genres        : chr  "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
##  $ Last.Updated  : chr  "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
##  $ Current.Ver   : chr  "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
##  $ Android.Ver   : chr  "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...
Table: Statistics summary.
App Category Rating Reviews Size Installs Type Price Content.Rating Genres Last.Updated Current.Ver Android.Ver
Min Length:9659 Length:9659 Min. :1.000 Min. : 0 Min. : 0.0085 Min. :0.000e+00 Length:9659 Min. : 0.000 Length:9659 Length:9659 Length:9659 Length:9659 Length:9659
Q1 Class :character Class :character 1st Qu.:4.000 1st Qu.: 25 1st Qu.: 5.3000 1st Qu.:1.000e+03 Class :character 1st Qu.: 0.000 Class :character Class :character Class :character Class :character Class :character
Median Mode :character Mode :character Median :4.300 Median : 967 Median : 13.1000 Median :1.000e+05 Mode :character Median : 0.000 Mode :character Mode :character Mode :character Mode :character Mode :character
Mean NA NA Mean :4.173 Mean : 216593 Mean : 20.1512 Mean :7.778e+06 NA Mean : 1.099 NA NA NA NA NA
Q3 NA NA 3rd Qu.:4.500 3rd Qu.: 29401 3rd Qu.: 27.0000 3rd Qu.:1.000e+06 NA 3rd Qu.: 0.000 NA NA NA NA NA
Max NA NA Max. :5.000 Max. :78158306 Max. :100.0000 Max. :1.000e+09 NA Max. :400.000 NA NA NA NA NA
NA NA NA NA’s :1463 NA NA NA NA NA NA NA NA NA NA

There are 1463 missing values in rating.

Handle rating missing values

#Replace NA in Ratings with Overall Mean
data_clean <- data_clean %>%
  mutate(Rating = ifelse(is.na(Rating), mean(Rating, na.rm = TRUE), Rating))

xkablesummary(data_clean)
Table: Statistics summary.
App Category Rating Reviews Size Installs Type Price Content.Rating Genres Last.Updated Current.Ver Android.Ver
Min Length:9659 Length:9659 Min. :1.000 Min. : 0 Min. : 0.0085 Min. :0.000e+00 Length:9659 Min. : 0.000 Length:9659 Length:9659 Length:9659 Length:9659 Length:9659
Q1 Class :character Class :character 1st Qu.:4.000 1st Qu.: 25 1st Qu.: 5.3000 1st Qu.:1.000e+03 Class :character 1st Qu.: 0.000 Class :character Class :character Class :character Class :character Class :character
Median Mode :character Mode :character Median :4.200 Median : 967 Median : 13.1000 Median :1.000e+05 Mode :character Median : 0.000 Mode :character Mode :character Mode :character Mode :character Mode :character
Mean NA NA Mean :4.173 Mean : 216593 Mean : 20.1512 Mean :7.778e+06 NA Mean : 1.099 NA NA NA NA NA
Q3 NA NA 3rd Qu.:4.500 3rd Qu.: 29401 3rd Qu.: 27.0000 3rd Qu.:1.000e+06 NA 3rd Qu.: 0.000 NA NA NA NA NA
Max NA NA Max. :5.000 Max. :78158306 Max. :100.0000 Max. :1.000e+09 NA Max. :400.000 NA NA NA NA NA

As there are many missing values in Rating, they are replaced with mean of Rating instead of dropping all he columns and there are no missing values in Reviews

Checking for Outliers For rating by seeing frequency for each rating

 breaks = seq(15,20,by = 1)
frequency_table = table(data_clean$Rating)
frequency_table
## 
##                1              1.2              1.4              1.5 
##               16                1                3                3 
##              1.6              1.7              1.8              1.9 
##                4                8                8               11 
##                2              2.1              2.2              2.3 
##               12                8               14               20 
##              2.4              2.5              2.6              2.7 
##               19               20               24               23 
##              2.8              2.9                3              3.1 
##               40               45               81               69 
##              3.2              3.3              3.4              3.5 
##               63              100              126              156 
##              3.6              3.7              3.8              3.9 
##              167              224              286              359 
##                4              4.1 4.17324304538799              4.2 
##              513              621             1463              810 
##              4.3              4.4              4.5              4.6 
##              897              895              848              683 
##              4.7              4.8              4.9                5 
##              442              221               85              271

From above it can be seen all the rating are between 1 and 5 and most of them lie from 3-5

Category

Checking type and unique values of Category column

# Checking the type of the Category 
typeof(data_apps$Category)
## [1] "character"
length(unique(data_clean$Category))
## [1] 33
length(unique(data_clean$Genres))
## [1] 118

There are 33 categories in the the data frame with 118 genres. This means that in each category, there are multiple genres. Given that, the later analyses in this project can be proceeded with Category variable.

Below is the graph for the distribution of Categories for the dataset after removing duplicates.

Current Version, Genres

Due to the inconsistent formatting of values in the Current.Vercolumn, and genre column which is same as Category column, these columns will be dropped and excluded from the analysis.

data_final <- data_clean %>% select(-c('Genres', 'Current.Ver'))

Category and Android version

Fromatted Category and Android Version columns

data_final$Category <- factor(data_final$Category)
data_final$Android.Ver <- factor(data_final$Android.Ver)
xkablesummary(data_final)
Table: Statistics summary.
App Category Rating Reviews Size Installs Type Price Content.Rating Last.Updated Android.Ver
Min Length:9659 FAMILY :1832 Min. :1.000 Min. : 0 Min. : 0.0085 Min. :0.000e+00 Length:9659 Min. : 0.000 Length:9659 Length:9659 4.1 and up :2202
Q1 Class :character GAME : 959 1st Qu.:4.000 1st Qu.: 25 1st Qu.: 5.3000 1st Qu.:1.000e+03 Class :character 1st Qu.: 0.000 Class :character Class :character 4.0.3 and up :1395
Median Mode :character TOOLS : 827 Median :4.200 Median : 967 Median : 13.1000 Median :1.000e+05 Mode :character Median : 0.000 Mode :character Mode :character 4.0 and up :1285
Mean NA BUSINESS : 420 Mean :4.173 Mean : 216593 Mean : 20.1512 Mean :7.778e+06 NA Mean : 1.099 NA NA Varies with device: 990
Q3 NA MEDICAL : 395 3rd Qu.:4.500 3rd Qu.: 29401 3rd Qu.: 27.0000 3rd Qu.:1.000e+06 NA 3rd Qu.: 0.000 NA NA 4.4 and up : 818
Max NA PERSONALIZATION: 376 Max. :5.000 Max. :78158306 Max. :100.0000 Max. :1.000e+09 NA Max. :400.000 NA NA 2.3 and up : 616
NA NA (Other) :4850 NA NA NA NA NA NA NA NA (Other) :2353
str(data_final$Category)
##  Factor w/ 33 levels "ART_AND_DESIGN",..: 1 1 1 1 1 1 1 1 1 1 ...
str(data_final$Android.Ver)
##  Factor w/ 34 levels "1.0 and up","1.5 and up",..: 16 16 16 19 21 9 16 19 11 16 ...

Content Rating, Last Updated

Format Content Rating and Last Updated columns

# Remove leading and trailing spaces and convert all text to a consistent format 
data_final$Content.Rating <- trimws(tolower(data_final$Content.Rating))


# Convert Last Updated to Date format
data_final$Last.Updated <- as.Date(data_final$Last.Updated, format = "%B %d, %Y")

# Verify the cleaning
print("\nSummary of Last.Updated after cleaning:")
## [1] "\nSummary of Last.Updated after cleaning:"
print(c(summary(data_clean$Last.Updated),summary(data_clean$Content.Rating)))
##      Length       Class        Mode      Length       Class        Mode 
##      "9659" "character" "character"      "9659" "character" "character"
str(data_clean$Last.Updated)
##  chr [1:9659] "January 7, 2018" "January 15, 2018" "August 1, 2018" ...

Checking missing values

cr_missing <- sum(is.na(data_final$`Content Rating`))
lu_missing <- sum(is.na(data_final$Last.Updated))
print(paste("Number of missing values in 'Content Rating':", cr_missing))
## [1] "Number of missing values in 'Content Rating': 0"
print(paste("Number of missing values in 'Last updated':", lu_missing))
## [1] "Number of missing values in 'Last updated': 0"
summary(data_apps)
##      App              Category             Rating         Reviews         
##  Length:10841       Length:10841       Min.   : 1.000   Length:10841      
##  Class :character   Class :character   1st Qu.: 4.000   Class :character  
##  Mode  :character   Mode  :character   Median : 4.300   Mode  :character  
##                                        Mean   : 4.193                     
##                                        3rd Qu.: 4.500                     
##                                        Max.   :19.000                     
##                                        NA's   :1474                       
##      Size             Installs             Type              Price          
##  Length:10841       Length:10841       Length:10841       Length:10841      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##  Content.Rating        Genres          Last.Updated       Current.Ver       
##  Length:10841       Length:10841       Length:10841       Length:10841      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##  Android.Ver       
##  Length:10841      
##  Class :character  
##  Mode  :character  
##                    
##                    
##                    
## 
describe(data_apps)
##                 vars     n    mean      sd median trimmed     mad min  max
## App*               1 10841 4904.30 2789.34 4978.0 4917.37 3580.48   1 9660
## Category*          2 10841   18.72    8.35   16.0   18.74    8.90   1   34
## Rating             3  9367    4.19    0.54    4.3    4.26    0.30   1   19
## Reviews*           4 10841 2744.18 1836.39 2747.0 2722.12 2343.99   1 6002
## Size*              5 10841  208.99  155.42  168.0  201.40  167.53   1  462
## Installs*          6 10841   10.23    4.96   10.0    9.81    4.45   1   22
## Type*              7 10841    2.15    0.52    2.0    2.00    0.00   1    4
## Price*             8 10841   88.08   15.97   92.0   92.00    0.00   1   93
## Content.Rating*    9 10841    3.46    1.01    3.0    3.21    0.00   1    7
## Genres*           10 10841   65.88   33.06   69.0   67.33   43.00   1  120
## Last.Updated*     11 10841  631.82  337.91  653.0  626.04  346.93   1 1378
## Current.Ver*      12 10841 1296.22  962.30 1112.0 1253.85 1235.01   1 2834
## Android.Ver*      13 10841   19.62    7.47   19.0   19.10    4.45   1   35
##                 range  skew kurtosis    se
## App*             9659 -0.03    -1.19 26.79
## Category*          33  0.12    -1.12  0.08
## Rating             18  0.60    65.94  0.01
## Reviews*         6001  0.06    -1.23 17.64
## Size*             461  0.46    -1.19  1.49
## Installs*          21  0.54    -0.64  0.05
## Type*               3  3.26     8.62  0.01
## Price*             92 -4.34    18.16  0.15
## Content.Rating*     6  1.88     1.81  0.01
## Genres*           119 -0.27    -0.98  0.32
## Last.Updated*    1377  0.03    -0.71  3.25
## Current.Ver*     2833  0.33    -1.32  9.24
## Android.Ver*       34  0.68     0.12  0.07

Data Exploring and Visualization

Univariate Analysis

Visualization for Price Distribution

# Count Plot for the Price distribution
ggplot(data_final, aes(x=Price)) +
  geom_histogram(binwidth=2, fill="pink", color="black") +
   xlim(0, 500) + ylim(0, 500) +
  labs(title="Price Distribution", x="Price", y="Frequency") +
  theme_minimal()

The data is highly skewed as there are many zero price entries.

# Boxplot for the same
ggplot(data_final, aes(y=Price)) +
  geom_boxplot(outlier.colour = "red", outlier.shape = 16, outlier.size = 1, fill="pink", color="black") +
  labs(title="Price Boxplot", y="Price") +
  theme_minimal()

Checking outliers for Price

outlierKD2 <- function(df, var, rm = FALSE, boxplt = FALSE, histogram = TRUE, qqplt = FALSE) {
  dt <- df  # Duplicate the dataframe for potential alteration
  var_name <- eval(substitute(var), eval(dt))
  na1 <- sum(is.na(var_name))
  m1 <- mean(var_name, na.rm = TRUE)
  colTotal <- boxplt + histogram + qqplt  # Calculate the total number of charts to be displayed
  par(mfrow = c(2, max(2, colTotal)), oma = c(0, 0, 3, 0))  # Adjust layout for plots
  
  # Q-Q plot with custom title
  if (qqplt) {
    qqnorm(var_name, main="Q-Q plot without Outliers")
    qqline(var_name)
  }
  
  # Histogram with custom title
  if (histogram) { 
    hist(var_name,main = "Histogram without Outliers", xlab = NA, ylab = NA) 
  }
  
  # Box plot with custom title
  if (boxplt) { 
    boxplot(var_name, main= "Box Plot without Outliers")
  }
  
  # Identify outliers
  outlier <- boxplot.stats(var_name)$out
  mo <- mean(outlier)
  var_name <- ifelse(var_name %in% outlier, NA, var_name)
  
  # Q-Q plot without outliers
  if (qqplt) {
    qqnorm(var_name, main="Q-Q plot with Outliers")
    qqline(var_name)
  }
  
  # Histogram without outliers
  if (histogram) { 
    hist(var_name, main = "Histogram with Outliers", xlab = NA, ylab = NA) 
  }
  
  # Box plot without outliers
  if (boxplt) { 
    boxplot(var_name, main = "Boxplot with Outliers") 
  }
  
  # Add the title for the overall plot section if any plots are displayed
  if (colTotal > 0) {
    title("Outlier Check", outer = TRUE)
    na2 <- sum(is.na(var_name))
    cat("Outliers identified:", na2 - na1, "\n")
    cat("Proportion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name)) * 100, 1), "\n")
    cat("Mean of the outliers:", round(mo, 2), "\n")
    cat("Mean without removing outliers:", round(m1, 2), "\n")
    cat("Mean if we remove outliers:", round(mean(var_name, na.rm = TRUE), 2), "\n")
  }
}
#outlier function is defined in previous chunck of code.
outlier_check_price = outlierKD2(data_final, Price, rm = FALSE, boxplt = TRUE, qqplt = TRUE)

## Outliers identified: 756 
## Proportion (%) of outliers: 8.5 
## Mean of the outliers: 14.05 
## Mean without removing outliers: 1.1 
## Mean if we remove outliers: 0

The price values in the dataset, including both typical and extreme values, are valid observations for our analysis. As such, removing these outliers may not be beneficial for our study.

#To check the value ranges
table(data_final$Price)
## 
##      0   0.99      1   1.04    1.2   1.26   1.29   1.49    1.5   1.59   1.61 
##   8903    145      3      1      1      1      1     46      1      1      1 
##    1.7   1.75   1.76   1.96   1.97   1.99      2   2.49    2.5   2.56   2.59 
##      2      1      1      1      1     73      3     25      1      1      1 
##    2.6    2.9   2.95   2.99   3.02   3.04   3.08   3.28   3.49   3.61   3.88 
##      1      1      1    124      1      1      1      1      7      1      1 
##    3.9   3.95   3.99   4.29   4.49   4.59    4.6   4.77    4.8   4.84   4.85 
##      1      1     57      1      9      1      1      1      1      1      1 
##   4.99      5   5.49   5.99   6.49   6.99   7.49   7.99   8.49   8.99      9 
##     70      1      5     26      5     11      2      7      2      5      1 
##   9.99     10  10.99  11.99  12.99  13.99     14  14.99  15.46  15.99  16.99 
##     19      2      2      3      4      2      1      9      1      1      2 
##  17.99  18.99   19.4   19.9  19.99  24.99  25.99  28.99  29.99  30.99  33.99 
##      2      1      1      1      5      3      1      1      5      1      1 
##  37.99  39.99  46.99  74.99  79.99  89.99 109.99 154.99    200 299.99 379.99 
##      1      2      1      1      1      1      1      1      1      1      1 
## 389.99 394.99 399.99    400 
##      1      1     12      1

As aldready mentioned, there are 8903 free apps (More apps with price as 0).

Visualization for Type Distribution

# Bar Plot for the Type Distribution
ggplot(data_final, aes(x = Type)) +
  geom_bar(fill = "pink", color = "black") +
  labs(title = "Distribution of App Types (Free vs Paid)", x = "Type", y = "Count") +
  theme_minimal()

As it is clear, there are more free apps.

Checking mean price for each type of Pricing

#Display statistics for the Price of apps grouped by their Type
data_final$Type <- as.factor(data_final$Type)


summary_by_type <- data.frame(
  Type = levels(data_final$Type),
  Min_Price = tapply(data_clean$Price, data_clean$Type, min, na.rm = TRUE),
  Max_Price = tapply(data_clean$Price, data_clean$Type, max, na.rm = TRUE),
  Mean_Price = tapply(data_clean$Price, data_clean$Type, mean, na.rm = TRUE),
  Median_Price = tapply(data_clean$Price, data_clean$Type, median, na.rm = TRUE)
)


print(summary_by_type)
##      Type Min_Price Max_Price Mean_Price Median_Price
## Free Free      0.00         0    0.00000         0.00
## NaN   NaN      0.00         0    0.00000         0.00
## Paid Paid      0.99       400   14.04515         2.99

Distribution for Price by Type

#Scatter plot for price distribution by app type
ggplot(data_final, aes(x = Type, y = Price, fill = Type)) +
  geom_boxplot() +
  labs(title = "Price Distribution by App Type", 
       x = "App Type", 
       y = "Price ($)") +
  theme_minimal()

Histogram for price distribution by App Type

ggplot(data_final, aes(x = Price, fill = Type)) +
  geom_histogram(binwidth = 60, alpha = 0.7, position = "identity") +
  facet_wrap(~ Type) +
  labs(title = "Price Distribution by App Type", 
       x = "Price ($)", 
       y = "Count") +
  theme_minimal()

Upon analyzing the price distribution across different app types, we found that some values in the Type column do not accurately represent the app prices (from above plot). Since we can fully rely on the Price values for our analysis, the Type column is seemed unnecessary.

Hence, Removing the Type column…

Dropping the Type column

#Using subset function
data_final <- subset(data_final, select = -Type)

#After removing the Type column and duplicated values
str(data_final)
## 'data.frame':    9659 obs. of  10 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : Factor w/ 33 levels "ART_AND_DESIGN",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : num  159 967 87510 215644 967 ...
##  $ Size          : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs      : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Price         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Content.Rating: chr  "everyone" "everyone" "everyone" "teen" ...
##  $ Last.Updated  : Date, format: "2018-01-07" "2018-01-15" ...
##  $ Android.Ver   : Factor w/ 34 levels "1.0 and up","1.5 and up",..: 16 16 16 19 21 9 16 19 11 16 ...
The Type column is successfully removed.

Let’s do bivariate analysis on price and other variables starting from here.

Visulization for Distribution of Installs

# Bar plot for distribution of Installs
# Create a new data frame to store the factor levels
data_clean1_factor <- data_final  

data_clean1_factor$Installs <- factor(data_final$Installs)

# Define new breaks for more even intervals for Installs
install_breaks <- c(0,500, 1000, 5000, 10000,50000, 100000, 300000, 1000000,5000000, 10000000,Inf)

# Create a categorical variable for installs based on these breaks
data_clean1_factor$Installs_Category <- cut(
  as.numeric(as.character(data_final$Installs)), 
  breaks = install_breaks, 
  right = FALSE, 
  labels = c("0+", "500+", "1K+", "5K+", "10K+",  "50K+", "100K+", "300K+", "1M+","5M+","Above 10M+")
)

# Plot the categorized Installs data
library(ggplot2)
ggplot(data_clean1_factor, aes(x = Installs_Category)) +
  geom_bar() +
  xlab("Installs") +
  ylab("Count") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  ggtitle("Distribution of App Installs by Category")

Visualization for Rating Distribution

boxplot(data_final$Rating,ylab = "Rating", xlab = "Count",col = "Blue")

hist(data_clean$Rating, main="Histogram of Apps Rating after cleaning", xlab="Rating (count)", col = 'blue', breaks = 100 )

qqnorm(data_clean$Rating)
qqline(data_clean$Rating, col = "red")

Here, it could be seen the plots are much clearer but still skewed due to other outliers from 1-3 rating but as these may be the reason from which we could find why the apps are low rated hencecannot be removed from our dataset.

Visualization for Reviews

boxplot(data_final$Reviews,ylab = "Reviews", xlab = "Count",col = 'Blue')

hist(data_final$Reviews, main="Histogram of Apps Reviews", xlab="Reviews (count)", col = 'blue', breaks = 100 )

ggplot(data_final, aes(x = log(Reviews))) +
  geom_histogram(binwidth = 0.1, fill = "blue", color = "black") +
  labs(title = "Log-Transformed Histogram of Ratings", x = "Log(Rating)", y = "Count")

qqnorm(data_final$Reviews)
qqline(data_final$Reviews, col = "red")

Similar to the case of ratings the plots are skewed due to the outliers. Hence, we can use the log plot of reviews for the visualisation which is normalised version of Reviews. As they are skewed, they donot follow normal distribution.

Review frequency table

xkablesummary(data_final)
Table: Statistics summary.
App Category Rating Reviews Size Installs Price Content.Rating Last.Updated Android.Ver
Min Length:9659 FAMILY :1832 Min. :1.000 Min. : 0 Min. : 0.0085 Min. :0.000e+00 Min. : 0.000 Length:9659 Min. :2010-05-21 4.1 and up :2202
Q1 Class :character GAME : 959 1st Qu.:4.000 1st Qu.: 25 1st Qu.: 5.3000 1st Qu.:1.000e+03 1st Qu.: 0.000 Class :character 1st Qu.:2017-08-05 4.0.3 and up :1395
Median Mode :character TOOLS : 827 Median :4.200 Median : 967 Median : 13.1000 Median :1.000e+05 Median : 0.000 Mode :character Median :2018-05-04 4.0 and up :1285
Mean NA BUSINESS : 420 Mean :4.173 Mean : 216593 Mean : 20.1512 Mean :7.778e+06 Mean : 1.099 NA Mean :2017-10-30 Varies with device: 990
Q3 NA MEDICAL : 395 3rd Qu.:4.500 3rd Qu.: 29401 3rd Qu.: 27.0000 3rd Qu.:1.000e+06 3rd Qu.: 0.000 NA 3rd Qu.:2018-07-17 4.4 and up : 818
Max NA PERSONALIZATION: 376 Max. :5.000 Max. :78158306 Max. :100.0000 Max. :1.000e+09 Max. :400.000 NA Max. :2018-08-08 2.3 and up : 616
NA NA (Other) :4850 NA NA NA NA NA NA NA (Other) :2353
outlierKD2(data_final, Reviews)
## Outliers identified: 1656 
## Proportion (%) of outliers: 20.7 
## Mean of the outliers: 1228141 
## Mean without removing outliers: 216592.6 
## Mean if we remove outliers: 7280.61

To check which are outliers lets make sections of data that is create bins to check which bins have maximum data, this would help us see how reviews are distributed.

Binned reviews

Binning into equal count in each bin to check averge rating for each bin

# Define the new custom breaks for bins
# Ensure there are no NA values


# Define new breaks for more even intervals
breaks <- c(0, 100, 500, 1000, 2500, 5000, 10000, 25000,50000,100000, 300000,1000000,Inf)

# Create a categorical variable based on the new breaks
Review_Category <- cut(data_final$Reviews, breaks = breaks, right = FALSE, 
                   labels = c("0+","100+", "500+", "1K+",
                              "2.5K+", "5K+", "10K+","25K+",
                              "50K+", "100K+","300K+","1M+"))

# Count the number of values in each bin
bin_counts <- as.data.frame(table(Review_Category))

# Rename the columns for clarity
colnames(bin_counts) <- c("Review_Category", "Count")

# Print the counts
print(bin_counts)
##    Review_Category Count
## 1               0+  3327
## 2             100+  1065
## 3             500+   462
## 4              1K+   586
## 5            2.5K+   475
## 6              5K+   474
## 7             10K+   719
## 8             25K+   606
## 9             50K+   498
## 10           100K+   647
## 11           300K+   451
## 12             1M+   349
# Create a line plot of the binned counts
ggplot(bin_counts, aes(x = Review_Category, y = Count, group = 1)) +
  geom_line(color = "blue", size = 1) +
  geom_point(color = "blue", size = 3) +
  labs(title = "Count of Reviews by Review Category", 
       x = "Review Category", 
       y = "Count of Reviews") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

Hence, high reviews can be observed in less apps and less reviews can be observed in more apps which is expected.

Visulization for Installs Vs Size

ggplot(data_clean, aes(x = Size, y = log(Installs))) +
  geom_hex(bins = 30) +
  scale_fill_viridis_c() + # Adds color gradient
  labs(title = "Plot of App Size vs. Installs (Log Scale)",
       x = "Size (MB)",
       y = "Installs (Log Scale)") +
  theme_minimal()

Visualization for Category Distribution

category_counts <- table(data_final$Category)

# Convert to data frame for plotting
category_counts_df <- as.data.frame(category_counts)
colnames(category_counts_df) <- c("Category", "Frequency") 

ggplot(category_counts_df, aes(x = reorder(Category, Frequency), y = Frequency)) + 
  geom_bar(stat = "identity", fill = "#1f3374") +
  geom_text(aes(label = Frequency), vjust = 0.5, hjust=1, size=2.5, color='#f8c220') +
  coord_flip() +  
  labs(title = "Distribution of Categories", x = "Category", y = "Frequency") +
  theme_minimal() +
   theme()

AS it can be seen from the graph above, most of the apps in the dataset belong to the Family category, and Beauty has the least number of apps.

Visualization for Android Version

Below is the figure showing the distribution of Android versions.

extract_version <- function(version) {
  version <- tolower(version)  # Make lowercase for consistency

  
  # Extract the first version in case of ranges (e.g., "4.1 - 7.1.1" -> "4.1")
  first_version <- strsplit(version, "[- ]")[[1]][1]
  
  # Remove "and up" if present (e.g., "4.0 and up" -> "4.0")
  first_version <- gsub("and up", "", first_version)
  
  return(as.numeric(first_version))  # Convert to numeric
}



df_clean <- data_final %>%
  mutate(Android_Ver = sapply(Android.Ver, extract_version)) %>%
  filter(!is.na(Android_Ver))  # Remove rows with NA in Android_Ver

android_installs <- data_final %>% 
  group_by(Android.Ver) %>% 
  summarize(Total_Installs = sum(Installs, na.rm = TRUE))

data_final <- data_final %>% 
  filter(Android.Ver != 'NaN')



ggplot(data_final, aes(x = Android.Ver)) + 
  geom_bar(binwidth = 0.5, fill = "#1f3374", color='#efefef') + 
 # scale_x_continuous(breaks = seq(1, 8, by = 1.0)) +  # Set x-axis ticks from 1.0 to 8.0
  theme_minimal() + 
  labs(
    title = "Distribution of Android Versions", 
    x = "Android Version", 
    y = "Count"
  ) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

As it can be seen that, the minimum required Android Version for most apps is 4.0 and up.

It can be seen that most Android Version have ratings range between 4.0 and 5.0.

Distribution and Visualisation for Content.Rating

# Clean and prepare the Last Updated  and Content column
data_updated <- data_final %>%
  mutate(
    Content.Rating = as.factor(Content.Rating)
  )

# 1. Content Rating Distribution
content_rating_dist <- table(data_updated$Content.Rating)
print("Content Rating Distribution:")
## [1] "Content Rating Distribution:"
print(content_rating_dist)
## 
## adults only 18+        everyone    everyone 10+      mature 17+            teen 
##               3            7901             322             393            1036 
##         unrated 
##               2
# Bar plot for Content Rating
ggplot(data_final, aes(x = Content.Rating)) +
  geom_bar(fill = "skyblue") +
  geom_text(stat = "count", aes(label = ..count..), vjust = -0.5) +
  labs(title = "Distribution of App Content Ratings",
       x = "Content Rating",
       y = "Number of Apps") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Everyone is the most dominant Category with 81.82% of all apps and Adults 18+ being most least significant category with about 0.03% of overall app population

Bivariate Analysis

Visualization for Price vs Installs

#Plotting a scatter plot between Price and installs
ggplot(data_final, aes(x=Price, y=log(data_final$Installs))) +
  geom_point(color = 'red', size = 1, alpha = 0.5) + 
  geom_smooth(method = 'lm', color = 'blue', se = FALSE) +
  labs(title = "Price vs Installs", x = "Price (USD)", y = "Number of Installs") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

From the scatter plot, we can see that there are more number of installations with price value 0.

Visualisation of Mean Installs for each Price Category

For a better visualization, we are categorizing price values 0 as free apps and plotting abox plot.

# Categorize the apps as "Free" or "Paid" based on Price
Price_Category <- ifelse(data_final$Price == 0, "Free", "Paid")
str(data_final$Price)
##  num [1:9657] 0 0 0 0 0 0 0 0 0 0 ...
str(Price_Category)
##  chr [1:9657] "Free" "Free" "Free" "Free" "Free" "Free" "Free" "Free" ...
#str(log(data_clean$Installs))

# Box plot of Price Category vs. log-transformed Installs
ggplot(data_final, aes(x = Price_Category, y = log(data_final$Installs))) +
  geom_boxplot(fill = "lightblue", color = "darkblue", alpha = 0.6) +
  labs(title = "Price Categories vs. Log-Transformed Installs", 
       x = "Price Category", 
       y = "Log(Installs)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  

# Categorize the apps as "Free" or "Paid" based on Price
Price_Category <- ifelse(data_final$Price == 0, "Free", "Paid")
str(data_final$Price)
##  num [1:9657] 0 0 0 0 0 0 0 0 0 0 ...
str(Price_Category)
##  chr [1:9657] "Free" "Free" "Free" "Free" "Free" "Free" "Free" "Free" ...
#str(data_final$log(data_clean$Installs))

table(Price_Category)
## Price_Category
## Free Paid 
## 8902  755

“Free” apps tend to have more installs than “Paid” apps. The difference between the means on the log scale is estimated to be between 3.47 and 3.97.

# Add Price_Category to data_final
data_duplicate <- data_final
data_duplicate$Price_Category <- ifelse(data_final$Price == 0, "Free", "Paid")

# Filter out rows with Installs <= 0 before summarizing
summary_table <- data_duplicate %>%
  filter(Installs > 0) %>%
  group_by(Price_Category) %>%
  summarise(Average_Log_Installs = mean(log(Installs), na.rm = TRUE),
            Count = n())

# View the summarized table
kable(summary_table, format = "html", col.names = c("Price Category", "Mean Log(Installs)", "App Count")) %>%
  kable_styling(full_width = FALSE, position = "center")
Price Category Mean Log(Installs) App Count
Free 10.993280 8897
Paid 7.251418 745

Visualization for Price vs Reviews & Rating

# Plot Price vs. Reviews
ggplot(data_final, aes(x=Price, y=Reviews)) +
  geom_point(color = 'blue') +
  geom_smooth(method = 'lm', color = 'red', se = FALSE) +
  labs(title = "Price vs Reviews", x = "Price (USD)", y = "Number of Reviews") +
  theme_minimal() + 
  theme(
    panel.background = element_rect(fill = "white"),  # Set panel background to white
    plot.background = element_rect(fill = "white"),   # Set plot background to white
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

# Plot Price vs. Rating
ggplot(data_final, aes(x=Price, y=Rating)) +
  geom_point(color = 'green') +
  geom_smooth(method = 'lm', color = 'red', se = FALSE) +
  labs(title = "Price vs Rating", x = "Price (USD)", y = "Rating") +
  theme_minimal() + 
  theme(
    panel.background = element_rect(fill = "white"),  # Set panel background to white
    plot.background = element_rect(fill = "white"),   # Set plot background to white
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

ggplot(data_final, aes(x = Rating, fill = Price)) +
  geom_density(alpha = 0.5) + 
  labs(title = "Kernel Density Estimate of App Ratings by Price Category",
       x = "App Size (MB)", 
       y = "Density") +
  theme_minimal() 

Price vs Reviews with installation: Cheaper products tend to have more reviews, indicating higher popularity or more frequent purchases. In contrast, expensive products tend to have fewer reviews, possibly because fewer people buy higher-priced items.

Price vs Ratings with installation: Price does not strongly affect the average rating, but there is a slight trend where lower-priced products have more variation in ratings, while higher-priced products tend to receive more consistent ratings around 4. May be higher price apps are meeting the customer expectations.

Visualization for Price vs Reviews vs Installs

# Scatter plot of Price vs. Ratings with log_Installs as  color
ggplot(data_final, aes(x = Price, y = Rating,color = log(data_final$Installs))) +
  geom_point(alpha = 0.6) +
  scale_color_gradient(low = "blue", high = "red") +  
  labs(title = "Price vs. Ratings with Installs as Color by Price", 
       x = "Price", 
       y = "Rating", 
       color = "log(Installs)") +
  theme_minimal()

# Scatter plot of Price vs. Reviews with log_Installs as color
ggplot(data_final, aes(x = Price, y = Reviews,color = log(data_final$Installs))) +
  geom_point(alpha = 0.6) +
  scale_color_gradient(low = "darkgreen", high = "yellow") +  
  labs(title = "Price vs. reviewss with Installs as Color by Price", 
       x = "Price", 
       y = "Reviews", 
       color = "log(Installs)") +
  theme_minimal()

# Create a KDE plot for Installs based on Price_Category
ggplot(data_final, aes(x = log(Installs), fill = Price)) +
  geom_density(alpha = 0.5) + 
  labs(title = "Kernel Density Estimate of Installations by Price Category",
       x = "App Size (MB)", 
       y = "Density") +
  theme_minimal() 

Concluding: Apps with lower prices, have more ratings and installs while apps priced higher tend to have fewer installs and more scattered ratings. Similarly, for reviews.

Visualization for Price vs Size

# Plot Price vs Size
ggplot(data_final, aes(x=Price, y=Size)) +
  geom_point(color = 'red') + 
  geom_smooth(method = 'lm', color = 'blue', se = FALSE) +
  labs(title = "Price vs Size", x = "Price (USD)", y = "App Size (MB)") +
  theme_minimal() 

# Create a KDE plot for Size based on Price_Category
ggplot(data_final, aes(x = Size, fill = Price)) +
  geom_density(alpha = 0.5) + 
  labs(title = "Kernel Density Estimate of App Size by Price Category",
       x = "App Size (MB)", 
       y = "Density") +
  theme_minimal() 

Boxplots for Rating vs Reviews

boxplot( data_clean$Rating~ Review_Category, data = data_clean, 
        main = "Boxplot of Review Counts by Review Category", 
        xlab = "Review Category", 
        ylab = "Review Rating",
        las = 2,        # Rotate the x-axis labels for readability
        col = "lightblue")  # Optional: Set color for the boxplots

In this we could observe that, as reviews increase the median of rating increased and the values clustered around higher ratings which could show that high reviews, could mean a better rated app.

Mean value of Ratings for each Review bins

# Calculate the mean Rating for each Review_Category
mean_ratings <- tapply(data_clean$Rating, Review_Category, mean, na.rm = TRUE)

# Convert the result to a data frame for better readability
mean_ratings_df <- data.frame(Review_Category = names(mean_ratings), Mean_Rating = as.numeric(mean_ratings))

# Print the mean ratings for each review bin
print(mean_ratings_df)
##    Review_Category Mean_Rating
## 1               0+    4.126221
## 2             100+    4.029538
## 3             500+    4.063188
## 4              1K+    4.107030
## 5            2.5K+    4.129572
## 6              5K+    4.191139
## 7             10K+    4.221836
## 8             25K+    4.231848
## 9             50K+    4.293775
## 10           100K+    4.329830
## 11           300K+    4.375610
## 12             1M+    4.426361
# Define correct order of Review_Category as a factor
mean_ratings_df$Review_Category <- factor(mean_ratings_df$Review_Category, 
                                          levels = c("0+","100+", "500+", "1K+",
                                                     "2.5K+", "5K+", "10K+","25K+",
                                                     "50K+", "100K+", "300K+", "1M+"))

# Plot the mean ratings for each review bin in the correct order
ggplot(mean_ratings_df, aes(x = Review_Category, y = Mean_Rating)) +
  geom_bar(stat = "identity", fill = "steelblue") +  # Use bar plot
  labs(title = "Mean Rating by Review Category",
       x = "Review Category",
       y = "Mean Rating") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

As we can see, the mean rating increases as the reviews increase.

Visualization for Reviews vs Installs

# Scatter plot for Installs vs Reviews
ggplot(data_clean1_factor, aes(x = Review_Category, y = Installs)) +
  geom_point(color = "blue", alpha = 0.5) +
  labs(title = "Scatter Plot of Installs vs Reviews", 
       x = "Number of Reviews", 
       y = "Number of Installs") +
  theme_minimal()

Visualization for Rating vs Installs

# Scatter plot of log-transformed Installs vs. Rating
ggplot(data_final, aes(x = log(data_final$Installs), y = Rating)) +
  geom_point(color = "blue", alpha = 0.6) +
  geom_smooth(method = "lm", color = "red", se = FALSE) +  # Add a regression line
  labs(title = "Log-Transformed Installs vs. Rating", 
       x = "Log(Installs)", 
       y = "Rating") +
  theme_minimal()

Visualization for Rating vs Installs by Category

Visualization for Category vs. Installs

Below is a boxplot show the distribution of number of installs for each category order by mean from highest to lowest.

ggplot(data_final, aes(x = reorder(Category, log(data_final$Installs),  FUN = mean), y = log(data_final$Installs))) +
  geom_boxplot(outlier.color = "#f05555", outlier.shape = 1, color='#1f3374') +  # Red outliers for emphasis
  coord_flip() +  # Flip for better readability
  scale_y_log10() +
  theme_minimal() +
  labs()

It can be seen from the graph that, on average, Entertainment apps receive the highest number of installations, followed by Education, Game, Photography, and Weather apps. In contrast, Art & Design apps have the fewest installations.

Visualization for Category vs. App Size

#convert_size <- function(size) {
#    size <- gsub(",", "", size)  # Remove commas
#    size <- tolower(size)  # Make lowercase for consistency
      
      # Handle "varies with device" by assigning NA
#    if (size == "varies with device") return(NA)
      
      # Convert "k" to MB (1 MB = 1000 KB)
 #   if (grepl("k", size)) return(as.numeric(gsub("k", "", size)) / 1000)
      
      # Convert "M" to numeric MB
  #  if (grepl("m", size)) return(as.numeric(gsub("m", "", size)))
      
      # Handle numeric values directly (e.g., "1000+")
   # if (grepl("\\d+\\+", size)) return(as.numeric(gsub("\\+", "", size)) / 1000)
      
      # Default case: return as numeric if possible
    #return(as.numeric(size))
    #}

Below is the figure showing the distribution of app sizes in each category.

#df_clean <- data_clean %>%
 # mutate(Size = sapply(Size, convert_size)) %>%
#  filter(!is.na(Size))

# Plot the histogram with faceting by category
ggplot(data_clean, aes(x = Size)) +
  geom_histogram(binwidth = 5, fill = "#304ba6", color = "black") +
  facet_wrap(~ Category, scales = "free_y") +
  theme_minimal() +
  labs(
    title = "Distribution of App Sizes by Category",
    x = "Size (MB)",
    y = "Count"
  ) +
  theme(
    strip.text = element_text(size = 5),
    axis.text.x = element_text(size = 7, angle = 45, hjust = 1)
  )

str(data_clean)
## 'data.frame':    9659 obs. of  13 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : chr  "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : num  159 967 87510 215644 967 ...
##  $ Size          : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs      : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Type          : chr  "Free" "Free" "Free" "Free" ...
##  $ Price         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Content.Rating: chr  "Everyone" "Everyone" "Everyone" "Teen" ...
##  $ Genres        : chr  "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
##  $ Last.Updated  : chr  "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
##  $ Current.Ver   : chr  "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
##  $ Android.Ver   : chr  "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...
ggplot(data_clean, aes(x = reorder(Category, Size, FUN = median), y = Size)) + 
  geom_boxplot(outlier.color = "#f05555", outlier.shape = 1) + 
  coord_flip() + 
  theme_minimal() + 
  labs(
    title = "Boxplot of App Sizes by Category (Ordered by Median)", 
    x = "Category", 
    y = "Size (MB)"
  ) + 
  theme(
    strip.text = element_text(size = 8), 
    axis.text.x = element_text(size = 7, angle = 45, hjust = 1)
  )

As it can be seen from the two figures above, most categories exhibit right-skewed app sizes, with the majority being under 50MB. However, the Game category stands out with a significantly larger median app size compared to other categories.

Visualization for Category vs. Reviews

Below is the graph displaying the distribution of reviews left by users for each category.

#df_aggregated
df_aggregated <- data_final %>% 
  group_by(Category) %>% 
  summarise(Total_Reviews = sum(Reviews, na.rm = TRUE))



# Plot the total reviews by category using a bar chart
ggplot(df_aggregated, aes(x = reorder(Category, -Total_Reviews), y = log10(Total_Reviews))) + 
  geom_bar(stat = "identity", fill = "#1f3374") + 
  labs(
    title = "Log-Scaled Total Reviews by Category", 
    x = "Category", 
    y = "Log10(Total Number of Reviews)"
  ) + 
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

AS it can be seen that game,communication apps have most reviews while events apps have the least reviews.

Histogram for Category vs. Rating

Below is the figure demonstrating the distribution of number of rating for each category.

ggplot(data_final, aes(x = Rating)) + 
  geom_histogram(binwidth = 0.5, fill = "#1f3374", color='#efefef') + 
  facet_wrap(~ Category, scales = "free_y") +  # Facet by Category with independent y-axis
  scale_x_continuous(limits = c(1, 5), breaks = seq(1, 5, by = 0.5)) +  # Restrict x-axis to 1-5
  theme_minimal() + 
  labs(
    title = "Distribution of Ratings by Category", 
    x = "Rating", 
    y = "Count"
  ) + 
  theme(
    strip.text = element_text(size = 5),  # Adjust facet label size
    axis.text.x = element_text(size = 5, angle = 45, hjust = 1),  # Rotate x-axis labels
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  )

As illustrated in the graph above, all categories have app ratings that range between 4.0 and 5.0.

Bar plot for Android Version vs. Installs

Below is the graph showing the number of installs for each minimum required Android Version.

ggplot(data_final, aes(x = reorder(Android.Ver, Installs), y = Installs)) + 
  geom_bar(stat = "identity", fill = "#1f3374") + 
  scale_y_continuous(labels = scales::comma) +  # Format y-axis with commas
  theme_minimal() + 
  labs(
    title = "Total Installs by Android Version", 
    x = "Android Version", 
    y = "Total Installs"
  ) + 
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),  # Rotate x-axis text labels
    axis.text.y = element_text(size = 8),  # Adjust y-axis text size
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  )

It can be seen that the highest number of installation is when there is different requirements of the versions for the app to run.

Boxplot for Android Version vs. Reviews

Below is the distribution of reviews for each minimum required Android Version.

df_clean <- data_final %>% 
  filter(!is.na(Android.Ver) & !is.na(Reviews)) %>% 
  mutate(Scaled_Reviews = log10(Reviews + 1))

ggplot(df_clean, aes(x = reorder(Android.Ver, Scaled_Reviews, FUN = median), y = Scaled_Reviews)) + 
  geom_boxplot(outlier.color = "#f05555", outlier.shape = 1) +  # Boxplot with red outliers
  coord_flip() +  # Flip coordinates for better readability
  theme_minimal() + 
  labs(
    title = "Distribution of Scaled Reviews by Android Version", 
    x = "Android Version", 
    y = "Scaled Reviews (Log10)"
  ) + 
  theme(
    axis.text.y = element_text(size = 8),  # Adjust y-axis text size
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  )

It can be seen that the version from 4.1 to 7.1.1 have the highest number of reviews, while version from 5.0 to 7.1.1 have the least number of reviews.

Histogram for Android Version vs. Rating

Below is the plot showing the number of ratings for each Android Version.

ggplot(df_clean, aes(x = Rating, fill = Android.Ver)) + 
  geom_histogram(binwidth = 0.5, position = "stack", color = "black", alpha = 0.7) + 
  scale_x_continuous(breaks = seq(1, 5, by = 0.5)) +  # Set x-axis breaks
  theme_minimal() + 
  labs(
    title = "Histogram of Ratings by Android Version", 
    x = "Rating", 
    y = "Count"
  ) + 
  theme(
    axis.text.x = element_text(size = 8), 
    axis.text.y = element_text(size = 8), 
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  )

It can be seen that most Android Version have ratings range between 4.0 and 5.0.

# Last Updated Analysis
# Create summary of updates by month and year
updates_by_month <- data_updated %>%
  mutate(
    update_month = format(Last.Updated, "%Y-%m"),
    update_year = year(Last.Updated)
  ) %>%
  group_by(update_month) %>%
  summarize(count = n()) %>%
  arrange(update_month)
# Plot updates over time
#ggplot(updates_by_month, aes(x = as.Date(paste0(update_month, "-01")), y = count)) +
  #geom_line(color = "blue") +
  #geom_point(color = "red") +
  #labs(title = "Number of App Updates Over Time",
  #     x = "Date",
  #     y = "Number of Updates") +
  #theme_minimal() +
 # theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Content Rating and Update Frequency Relationship
update_frequency_by_rating <- data_updated %>%
  group_by(Content.Rating) %>%
  summarize(
    avg_last_update = mean(Last.Updated),
    median_last_update = median(Last.Updated),
    n_apps = n()
  )
print("\nUpdate Frequency by Content Rating:")
## [1] "\nUpdate Frequency by Content Rating:"
print(update_frequency_by_rating)
## # A tibble: 6 × 4
##   Content.Rating  avg_last_update median_last_update n_apps
##   <fct>           <date>          <date>              <int>
## 1 adults only 18+ 2018-07-20      2018-07-24              3
## 2 everyone        2017-10-20      2018-04-20           7901
## 3 everyone 10+    2017-11-24      2018-06-06            322
## 4 mature 17+      2018-02-18      2018-07-09            393
## 5 teen            2017-12-03      2018-06-05           1036
## 6 unrated         2013-10-25      2013-10-25              2
# Basic statistics for Installs by Content Rating
installs_by_rating <- data_updated %>%
  group_by(Content.Rating) %>%
  summarise(
    mean_installs = mean(Installs, na.rm = TRUE),
    median_installs = median(Installs, na.rm = TRUE),
    total_installs = sum(Installs, na.rm = TRUE),
    n_apps = n()
  ) %>%
  arrange(desc(mean_installs))

print("Summary of Installs by Content Rating:")
## [1] "Summary of Installs by Content Rating:"
print(installs_by_rating)
## # A tibble: 6 × 5
##   Content.Rating  mean_installs median_installs total_installs n_apps
##   <fct>                   <dbl>           <dbl>          <dbl>  <int>
## 1 teen                15914358.          500000    16487275393   1036
## 2 everyone 10+        12472894.         1000000     4016271795    322
## 3 everyone             6604144.           50000    52179341961   7901
## 4 mature 17+           6203529.          500000     2437986878    393
## 5 adults only 18+       666667.          500000        2000000      3
## 6 unrated                25250            25250          50500      2

Last updated vs Content Rating

# Required libraries
library(dplyr)
library(tidyr)
library(lubridate)
library(ggplot2)
library(reshape2)

# Create days_since_update and data preparation
data_updated <- data_final %>%
  mutate(
    # Convert Last.Updated to proper date format (assuming it's in standard format)
    last_updated = as.Date(Last.Updated),
    current_date = Sys.Date(),
    # Calculate days since last update
    days_since_update = as.numeric(difftime(current_date, last_updated, units = "days")),
    # Extract month from last_updated date
    update_month = month(last_updated)
  ) %>%
  # Remove any invalid dates or NA values
  filter(!is.na(last_updated), !is.na(days_since_update))

# Create subset for update analysis
data_updated <- data_updated %>% filter(!is.na(days_since_update))

# Calculate update statistics by Content Rating
update_patterns <- data_updated %>%
  group_by(Content.Rating) %>%
  summarize(
    avg_days_since_update = mean(days_since_update, na.rm = TRUE),
    median_days_since_update = median(days_since_update, na.rm = TRUE),
    sd_days_since_update = sd(days_since_update, na.rm = TRUE),
    n_apps = n(),
    cv = sd_days_since_update / avg_days_since_update * 100  # Coefficient of Variation
  ) %>%
  arrange(avg_days_since_update)

print("\nUpdate Patterns by Content Rating:")
## [1] "\nUpdate Patterns by Content Rating:"
print(update_patterns)
## # A tibble: 6 × 6
##   Content.Rating  avg_days_since_update median_days_since_update
##   <chr>                           <dbl>                    <dbl>
## 1 adults only 18+                 2291.                    2288 
## 2 mature 17+                      2444.                    2303 
## 3 teen                            2521.                    2337 
## 4 everyone 10+                    2530.                    2336 
## 5 everyone                        2565.                    2383 
## 6 unrated                         4020.                    4020.
## # ℹ 3 more variables: sd_days_since_update <dbl>, n_apps <int>, cv <dbl>
# Create monthly update counts
update_heatmap_data <- data_updated %>%
  group_by(update_month, Content.Rating) %>%
  summarize(count = n(), .groups = 'drop') %>%
  # Ensure all months and ratings are included, even if count is 0
  complete(
    update_month = 1:12,
    Content.Rating = unique(data_updated$Content.Rating),
    fill = list(count = 0)
  ) %>%
  # Reshape data for heatmap
  pivot_wider(
    names_from = Content.Rating,
    values_from = count
  )

# Convert to matrix for traditional heatmap
update_matrix <- as.matrix(update_heatmap_data[,-1])
rownames(update_matrix) <- month.abb[update_heatmap_data$update_month]

# Create enhanced heatmap using ggplot2
heatmap_data_long <- melt(update_matrix)
colnames(heatmap_data_long) <- c("Month", "Content_Rating", "Count")
heatmap_data_long$Month <- factor(heatmap_data_long$Month, levels = month.abb)

# Create the heatmap visualization
ggplot(heatmap_data_long, aes(x = Content_Rating, y = Month, fill = Count)) +
  geom_tile(color = "white") +  # Add white borders between tiles
  scale_fill_gradient(
    low = "white", 
    high = "steelblue", 
    name = "Number of Updates"
  ) +
  theme_minimal() +
  labs(
    title = "App Update Patterns by Content Rating",
    x = "Content Rating",
    y = "Month",
    subtitle = paste("Data as of", format(Sys.Date(), "%B %d, %Y"))
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(hjust = 0.5, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5),
    panel.grid = element_blank(),
    panel.border = element_rect(fill = NA, color = "grey80"),
    legend.position = "right"
  )

# Calculate update velocity
update_velocity <- data_updated %>%
  group_by(Content.Rating) %>%
  summarize(
    update_velocity = n() / n_distinct(update_month),
    total_apps = n(),
    avg_days_between_updates = mean(days_since_update, na.rm = TRUE)
  ) %>%
  arrange(desc(update_velocity))

print("\nUpdate Velocity by Content Rating:")
## [1] "\nUpdate Velocity by Content Rating:"
print(update_velocity)
## # A tibble: 6 × 4
##   Content.Rating  update_velocity total_apps avg_days_between_updates
##   <chr>                     <dbl>      <int>                    <dbl>
## 1 everyone                  658.        7901                    2565.
## 2 teen                       86.3       1036                    2521.
## 3 mature 17+                 32.8        393                    2444.
## 4 everyone 10+               26.8        322                    2530.
## 5 adults only 18+             1.5          3                    2291.
## 6 unrated                     1            2                    4020.
# Optional: Additional summary statistics for days since update
summary_stats <- data_updated %>%
  summarize(
    mean_days = mean(days_since_update, na.rm = TRUE),
    median_days = median(days_since_update, na.rm = TRUE),
    min_days = min(days_since_update, na.rm = TRUE),
    max_days = max(days_since_update, na.rm = TRUE),
    q1_days = quantile(days_since_update, 0.25, na.rm = TRUE),
    q3_days = quantile(days_since_update, 0.75, na.rm = TRUE)
  )

print("\nOverall Summary Statistics for Days Since Update:")
## [1] "\nOverall Summary Statistics for Days Since Update:"
print(summary_stats)
##   mean_days median_days min_days max_days q1_days q3_days
## 1  2554.227        2369     2273     5274    2295    2641

Observation for Update Frequency Velocity Analysis:

This column represents the average number of updates per app for each content rating category. It reflects how frequently apps in each category receive updates.

# # 1. Update Cycle Analysis
# data_updated <- data_updated %>%
#   mutate(
#     Last.Updated = as.Date(Last.Updated, format = "%B %d, %Y"),
#     day_of_week = wday(Last.Updated, label = TRUE),
#     week_of_year = week(Last.Updated),
#     month_of_year = month(Last.Updated, label = TRUE),
#     season = case_when(
#       month_of_year %in% c("Dec", "Jan", "Feb") ~ "Winter",
#       month_of_year %in% c("Mar", "Apr", "May") ~ "Spring",
#       month_of_year %in% c("Jun", "Jul", "Aug") ~ "Summer",
#       TRUE ~ "Fall"
#     )
#   )
# 
# # Day of Week Update Pattern by Content Rating
# dow_pattern <- data_updated %>%
#   group_by(Content.Rating, day_of_week) %>%
#   summarise(count = n()) %>%
#   group_by(Content.Rating) %>%
#   mutate(percentage = count/sum(count) * 100)
# 
# ggplot(dow_pattern, aes(x = day_of_week, y = percentage, fill = Content.Rating)) +
#   geom_bar(stat = "identity", position = "dodge") +
#   facet_wrap(~Content.Rating) +
#   labs(title = "Update Day Preferences by Content Rating",
#        x = "Day of Week",
#        y = "Percentage of Updates") +
#   theme_minimal() +
#   theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Update Interval Analysis
update_intervals <- data_updated %>%
  group_by(Content.Rating) %>%
  arrange(Last.Updated) %>%
  mutate(days_between_updates = as.numeric(Last.Updated - lag(Last.Updated))) %>%
  summarise(
    mean_interval = mean(days_between_updates, na.rm = TRUE),
    median_interval = median(days_between_updates, na.rm = TRUE),
    std_dev = sd(days_between_updates, na.rm = TRUE),
    cv = std_dev / mean_interval * 100  # Coefficient of Variation
  )

print("Update Interval Analysis:")
## [1] "Update Interval Analysis:"
print(update_intervals)
## # A tibble: 6 × 5
##   Content.Rating  mean_interval median_interval std_dev    cv
##   <chr>                   <dbl>           <dbl>   <dbl> <dbl>
## 1 adults only 18+        15                  15    7.07  47.1
## 2 everyone                0.380               0    3.53 929. 
## 3 everyone 10+            8.33                1   46.5  557. 
## 4 mature 17+              5.48                0   21.5  392. 
## 5 teen                    2.36                0   14.7  622. 
## 6 unrated              1213                1213   NA     NA
# Required libraries
library(dplyr)
library(tidyr)
library(lubridate)
library(ggplot2)
library(reshape2)
library(scales)

# Create data_updated with seasonal information while keeping data_final unchanged
data_updated <- data_final %>%
  mutate(
    last_updated = as.Date(Last.Updated),
    current_date = Sys.Date(),
    days_since_update = as.numeric(difftime(current_date, last_updated, units = "days")),
    update_month = month(last_updated),
    season = case_when(
      update_month %in% c(12, 1, 2) ~ "Winter",
      update_month %in% c(3, 4, 5) ~ "Spring",
      update_month %in% c(6, 7, 8) ~ "Summer",
      update_month %in% c(9, 10, 11) ~ "Fall"
    )
  ) %>%
  filter(!is.na(last_updated), !is.na(days_since_update))

# Calculate seasonal update intensity
seasonal_intensity <- data_updated %>%
  group_by(Content.Rating, season) %>%
  summarise(
    update_count = n(),
    update_intensity = n() / n_distinct(last_updated),
    avg_days_between_updates = mean(days_since_update, na.rm = TRUE),
    .groups = 'drop'
  ) %>%
  mutate(season = factor(season, levels = c("Winter", "Spring", "Summer", "Fall"))) %>%
  arrange(Content.Rating, desc(update_intensity))

# Create enhanced seasonal bar plot
seasonal_plot <- ggplot(seasonal_intensity, 
       aes(x = season, y = update_intensity, fill = Content.Rating)) +
  geom_bar(stat = "identity", position = "dodge", width = 0.8) +
  scale_fill_brewer(palette = "Set3") +
  labs(
    title = "Seasonal Update Intensity by Content Rating",
    subtitle = paste("Analysis Period:", format(min(data_updated$last_updated), "%B %Y"), 
                    "to", format(max(data_updated$last_updated), "%B %Y")),
    x = "Season",
    y = "Update Intensity",
    fill = "Content Rating"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    plot.subtitle = element_text(hjust = 0.5, size = 10),
    axis.text.x = element_text(angle = 0),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    legend.position = "right"
  )

# Create seasonal heatmap
seasonal_heatmap <- ggplot(seasonal_intensity, 
       aes(x = season, y = Content.Rating, fill = update_intensity)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(
    low = "white",
    high = "steelblue",
    name = "Update\nIntensity"
  ) +
  labs(
    title = "Seasonal Update Patterns Heatmap",
    x = "Season",
    y = "Content Rating"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold"),
    axis.text.x = element_text(angle = 0),
    panel.grid = element_blank(),
    legend.position = "right"
  )

# Print both plots side by side
library(gridExtra)
grid.arrange(seasonal_plot, seasonal_heatmap, ncol = 2)

# Print seasonal statistics
print("\nSeasonal Update Intensity Statistics:")
## [1] "\nSeasonal Update Intensity Statistics:"
print(seasonal_intensity)
## # A tibble: 19 × 5
##    Content.Rating  season update_count update_intensity avg_days_between_updates
##    <chr>           <fct>         <int>            <dbl>                    <dbl>
##  1 adults only 18+ Summer            3             1                       2291.
##  2 everyone        Summer         3991            11.1                     2430.
##  3 everyone        Spring         1825             5.42                    2591.
##  4 everyone        Winter         1202             3.70                    2751.
##  5 everyone        Fall            883             3.06                    2870.
##  6 everyone 10+    Summer          190             2.5                     2393.
##  7 everyone 10+    Spring           54             1.23                    2624.
##  8 everyone 10+    Fall             32             1.19                    2874 
##  9 everyone 10+    Winter           46             1.18                    2742.
## 10 mature 17+      Summer          269             3.90                    2332.
## 11 mature 17+      Spring           59             1.23                    2621.
## 12 mature 17+      Winter           44             1.1                     2674.
## 13 mature 17+      Fall             21             1.05                    2895.
## 14 teen            Summer          612             4.67                    2399.
## 15 teen            Spring          203             1.69                    2579.
## 16 teen            Winter          109             1.35                    2740.
## 17 teen            Fall            112             1.26                    2863.
## 18 unrated         Summer            1             1                       3414 
## 19 unrated         Winter            1             1                       4627
# Additional seasonal summary
seasonal_summary <- data_updated %>%
  group_by(season) %>%
  summarise(
    total_updates = n(),
    avg_days_since_update = mean(days_since_update, na.rm = TRUE),
    median_days_since_update = median(days_since_update, na.rm = TRUE),
    n_apps = n_distinct(Content.Rating),
    .groups = 'drop'
  ) %>%
  arrange(match(season, c("Winter", "Spring", "Summer", "Fall")))

print("\nOverall Seasonal Summary:")
## [1] "\nOverall Seasonal Summary:"
print(seasonal_summary)
## # A tibble: 4 × 5
##   season total_updates avg_days_since_update median_days_since_update n_apps
##   <chr>          <int>                 <dbl>                    <dbl>  <int>
## 1 Winter          1402                 2748.                    2506       5
## 2 Spring          2141                 2591.                    2401       4
## 3 Summer          5066                 2420.                    2297       6
## 4 Fall            1048                 2870.                    2602.      4
# Create stacked area chart for seasonal trends
seasonal_trend <- data_updated %>%
  group_by(season, Content.Rating) %>%
  summarise(
    update_count = n(),
    .groups = 'drop'
  ) %>%
  ggplot(aes(x = season, y = update_count, fill = Content.Rating)) +
  geom_area(position = "stack") +
  scale_fill_brewer(palette = "Set3") +
  labs(
    title = "Seasonal Update Distribution",
    x = "Season",
    y = "Number of Updates",
    fill = "Content Rating"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold"),
    axis.text.x = element_text(angle = 0),
    panel.grid.minor = element_blank()
  )

print(seasonal_trend)

The visualization shows the seasonal update intensity for various content ratings across different seasons (Fall, Spring, Summer, and Winter). The “Update Intensity” measures how frequently updates occurred, normalized by the number of distinct update events. The graph reveals that content rated as “everyone” consistently exhibits higher update intensity across all seasons, particularly peaking during the Summer. Other content ratings, such as “mature 17+” and “teen,” show notable but lower intensities, with a generally even distribution across seasons. This pattern suggests that applications rated for general audiences tend to undergo more frequent updates, especially during the Summer, potentially to meet increased demand or prepare for seasonal trends.

Visualization for Content Rating vs Installs

installs_by_rating <- data_updated %>%
  group_by(Content.Rating) %>%
  summarise(
    mean_installs = mean(Installs, na.rm = TRUE),
    median_installs = median(Installs, na.rm = TRUE),
    total_installs = sum(Installs, na.rm = TRUE),
    n_apps = n()
  ) %>%
  arrange(desc(mean_installs))

# Visualize distribution of installs by content rating
ggplot(data_updated, aes(x = Content.Rating, y = log10(Installs))) +
  geom_boxplot(fill = "lightblue") +
  labs(title = "Distribution of App Installs by Content Rating",
       x = "Content Rating",
       y = "Log10(Number of Installs)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Distribution of Installations by Update Recency and Content Rating

data_analysis <- data_updated %>%
  mutate(
    days_since_update = as.numeric(difftime(max(Last.Updated), Last.Updated, units = "days")),
    update_year = year(Last.Updated),
    update_month = month(Last.Updated)
  )


data_analysis <- data_analysis %>%
  mutate(update_recency = ifelse(days_since_update <= median(days_since_update),
                                "Recent Update", "Old Update"))

recent_vs_old <- data_analysis %>%
  group_by(Content.Rating, update_recency) %>%
  summarise(
    mean_installs = mean(Installs, na.rm = TRUE),
    median_installs = median(Installs, na.rm = TRUE),
    n_apps = n()
  )

print("\nComparison of Installs by Update Recency and Content Rating:")
## [1] "\nComparison of Installs by Update Recency and Content Rating:"
print(recent_vs_old)
## # A tibble: 10 × 5
## # Groups:   Content.Rating [6]
##    Content.Rating  update_recency mean_installs median_installs n_apps
##    <chr>           <chr>                  <dbl>           <dbl>  <int>
##  1 adults only 18+ Recent Update        666667.          500000      3
##  2 everyone        Old Update          1788041.           10000   4109
##  3 everyone        Recent Update      11822859.          500000   3792
##  4 everyone 10+    Old Update          2711120.          100000    135
##  5 everyone 10+    Recent Update      19520163.         1000000    187
##  6 mature 17+      Old Update           875646.          100000    118
##  7 mature 17+      Recent Update       8489675.          500000    275
##  8 teen            Old Update          1625562.           50000    441
##  9 teen            Recent Update      26504878.         1000000    595
## 10 unrated         Old Update            25250            25250      2
# 7. Visualization of update recency effect
ggplot(data_analysis, aes(x = Content.Rating, y = log10(Installs), fill = update_recency)) +
  geom_boxplot() +
  labs(title = "Install Distribution by Content Rating and Update Recency",
       x = "Content Rating",
       y = "Log10(Number of Installs)",
       fill = "Update Recency") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

The boxplot shows the distribution of app installs across different content ratings, segmented by update recency (old vs. recent).

Apps with recent updates generally have higher median installs compared to those with older updates, indicating that more frequently updated apps tend to attract more users.

This trend is evident across most content ratings, especially for categories like “everyone” and “teen,” where recent updates show a noticeable increase in the upper range of installs. For “everyone 10+” and “mature 17+,” the difference between old and recent updates is less pronounced, suggesting that the effect of update recency on installs might be weaker in these categories.

The “adults only 18+” and “unrated” categories still exhibit lower install numbers overall, regardless of update recency, highlighting the limited popularity of these app types.

Visualization for Last Updated vs Content Rating vs Installs

# 3. Timeline analysis: Average installs over time by content rating
installs_timeline <- data_updated %>%
  group_by(Content.Rating, Last.Updated) %>%
  summarise(avg_installs = mean(Installs, na.rm = TRUE)) %>%
  ungroup()

ggplot(installs_timeline, aes(x = Last.Updated, y = log10(avg_installs), color = Content.Rating)) +
  geom_smooth(method = "loess", se = FALSE) +
  labs(title = "Average App Installs Over Time by Content Rating",
       x = "Last Updated Date",
       y = "Log10(Average Installs)") +
  theme_minimal() +
  theme(legend.position = "bottom")

The line graph depicts the trend of average app installs over time for different content ratings, with the y-axis on a logarithmic scale (log10). The curves reveal that apps with broader content ratings like “everyone” and “everyone 10+” show significant growth in average installs, particularly from 2016 onwards, reaching a peak around 2018. This indicates a surge in popularity and possibly greater user engagement or app availability during that period. Similarly, “mature 17+” apps follow a parallel trend but start with higher average installs and decline around 2012 before recovering alongside the other categories.

The “teen” content rating exhibits a unique pattern with fluctuating growth, maintaining relatively steady installs before rising sharply from 2016 onwards. In contrast, “adults only 18+” shows a limited increase, suggesting that apps with this rating have a smaller user base. The convergence of all content ratings towards higher install averages near 2018 reflects an overall trend in the app market where app downloads increased across various content ratings.

summary(data_final)
##      App                       Category        Rating         Reviews        
##  Length:9657        FAMILY         :1832   Min.   :1.000   Min.   :       0  
##  Class :character   GAME           : 959   1st Qu.:4.000   1st Qu.:      25  
##  Mode  :character   TOOLS          : 827   Median :4.200   Median :     967  
##                     BUSINESS       : 420   Mean   :4.173   Mean   :  216637  
##                     MEDICAL        : 395   3rd Qu.:4.500   3rd Qu.:   29415  
##                     PERSONALIZATION: 374   Max.   :5.000   Max.   :78158306  
##                     (Other)        :4850                                     
##       Size             Installs             Price         Content.Rating    
##  Min.   :  0.0085   Min.   :0.000e+00   Min.   :  0.000   Length:9657       
##  1st Qu.:  5.3000   1st Qu.:1.000e+03   1st Qu.:  0.000   Class :character  
##  Median : 13.1000   Median :1.000e+05   Median :  0.000   Mode  :character  
##  Mean   : 20.1540   Mean   :7.779e+06   Mean   :  1.099                     
##  3rd Qu.: 27.0000   3rd Qu.:1.000e+06   3rd Qu.:  0.000                     
##  Max.   :100.0000   Max.   :1.000e+09   Max.   :400.000                     
##                                                                             
##   Last.Updated                    Android.Ver  
##  Min.   :2010-05-21   4.1 and up        :2202  
##  1st Qu.:2017-08-05   4.0.3 and up      :1395  
##  Median :2018-05-04   4.0 and up        :1285  
##  Mean   :2017-10-30   Varies with device: 990  
##  3rd Qu.:2018-07-17   4.4 and up        : 818  
##  Max.   :2018-08-08   2.3 and up        : 616  
##                       (Other)           :2351

Descriptive Statstics

The descriptive statistics for the Google Play Store dataset provide insights into app ratings, popularity, size, installation counts, and pricing.

  • Rating: App ratings range from 1 to 5, with an average rating of 4.17, indicating generally positive user feedback. Most ratings fall between 4 and 4.5.
  • Reviews: The number of reviews is highly skewed; while the average is over 216,000 reviews, the median is only 967, suggesting that a few popular apps have amassed the majority of reviews.
  • Size: App sizes vary widely, with a mean of 20.15 MB. Most apps fall within 5.3 to 27 MB, and the largest app size recorded is 100 MB.
  • Installs: The median app has 100,000 installs, while the mean is significantly higher at 7.78 million installs, highlighting that a small number of highly popular apps inflate the average.
  • Price: Most apps are free, with a median price of $0. A small percentage are paid, with prices ranging up to $400, though the mean price remains low at $1.10.
  • For categorical variables, apps are most commonly categorized under FAMILY, followed by GAME and TOOLS. Common Android version requirements are 4.1 and up and 4.0.3 and up.

Statistical Tests

Statistical test for Installs and Price

# Check for missing values and ensure no negative/zero values in log_Installs
#data_final <- data_final %>%
  #filter(!is.na(Installs), Installs > 0)  # Remove missing values and zeros in Installs

# Apply log transformation, adding 1 to avoid log(0)
#data_final$log_Installs <- log(data_final$Installs + 1)

# Ensure Price_Category has no missing values
#data_final <- data_final %>%
 #filter(!is.na(Price_Category))

#Perform t-test on log-transformed Installs by Price Category
#t_test_result <- t.test(log_Installs ~ Price_Category, data = data_final, var.equal = FALSE)

#Print t-test results
#print(t_test_result)

There is a statistically significant difference between the number of installs for “Free” and “Paid” apps, with the p-value being extremely small.

From the above analysis, we can practically state that free apps are more popular than paid apps, which can be considered true in the app market.

T-Test for Reviews and Price

#Confirming with a t-test
# Perform t-test for Reviews between Free and Paid
t_test_reviews <- t.test(Reviews ~ Price_Category, data = data_updated)

# Perform t-test for Rating between Free and Paid
t_test_rating <- t.test(Rating ~ Price_Category, data = data_updated)

# Print the results
print(t_test_reviews)
## 
##  Welch Two Sample t-test
## 
## data:  Reviews by Price_Category
## t = 11.018, df = 9298.8, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group Free and group Paid is not equal to 0
## 95 percent confidence interval:
##  185410.6 265657.1
## sample estimates:
## mean in group Free mean in group Paid 
##         234269.981           8736.139
print(t_test_rating)
## 
##  Welch Two Sample t-test
## 
## data:  Rating by Price_Category
## t = -3.9306, df = 882.09, p-value = 9.136e-05
## alternative hypothesis: true difference in means between group Free and group Paid is not equal to 0
## 95 percent confidence interval:
##  -0.11197486 -0.03739222
## sample estimates:
## mean in group Free mean in group Paid 
##           4.167347           4.242030
  • There is a statistically significant difference between the mean number of reviews for Free and Paid apps. Free apps have significantly more reviews on average.

  • There is a statistically significant difference between the mean ratings for Free and Paid apps. Paid apps have slightly higher ratings on average, though the difference is small.

ANOVA Test for Reviews vs Ratings

The tests below are to test whether or not different review categories have different average ratings.

anova_result <- aov(Rating ~ as.factor(Review_Category), data = data_clean)
summary(anova_result)
##                              Df Sum Sq Mean Sq F value Pr(>F)    
## as.factor(Review_Category)   11  106.3   9.662   41.36 <2e-16 ***
## Residuals                  9647 2253.6   0.234                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

According to p-value, it is significant hence we can say that the average rating for all review categories is not same.

Post Hoc Test

# Perform Tukey's HSD
tukey_result <- TukeyHSD(anova_result)
tukey_result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Rating ~ as.factor(Review_Category), data = data_clean)
## 
## $`as.factor(Review_Category)`
##                     diff          lwr         upr     p adj
## 100+-0+     -0.096683215 -0.152307271 -0.04105916 0.0000009
## 500+-0+     -0.063032835 -0.141474646  0.01540898 0.2646281
## 1K+-0+      -0.019190832 -0.089971134  0.05158947 0.9992526
## 2.5K+-0+     0.003350463 -0.074143085  0.08084401 1.0000000
## 5K+-0+       0.064918154 -0.012646893  0.14248320 0.2087515
## 10K+-0+      0.095614797  0.030638525  0.16059107 0.0000973
## 25K+-0+      0.105627098  0.035846939  0.17540726 0.0000488
## 50K+-0+      0.167554014  0.091642554  0.24346547 0.0000000
## 100K+-0+     0.203608898  0.135724795  0.27149300 0.0000000
## 300K+-0+     0.249388670  0.170111342  0.32866600 0.0000000
## 1M+-0+       0.300139945  0.211244127  0.38903576 0.0000000
## 500+-100+    0.033650380 -0.054364565  0.12166533 0.9848292
## 1K+-100+     0.077492383 -0.003768703  0.15875347 0.0784345
## 2.5K+-100+   0.100033678  0.012862795  0.18720456 0.0096675
## 5K+-100+     0.161601369  0.074366918  0.24883582 0.0000001
## 10K+-100+    0.192298012  0.116039053  0.26855697 0.0000000
## 25K+-100+    0.202310313  0.121918874  0.28270175 0.0000000
## 50K+-100+    0.264237229  0.178469737  0.35000472 0.0000000
## 100K+-100+   0.300292113  0.221540831  0.37904339 0.0000000
## 300K+-100+   0.346071885  0.257311491  0.43483228 0.0000000
## 1M+-100+     0.396823160  0.299375844  0.49427048 0.0000000
## 1K+-500+     0.043842003 -0.054455739  0.14213974 0.9515761
## 2.5K+-500+   0.066383298 -0.036853541  0.16962014 0.6214468
## 5K+-500+     0.127950989  0.024660470  0.23124151 0.0030189
## 10K+-500+    0.158647632  0.064443010  0.25285225 0.0000025
## 25K+-500+    0.168659933  0.071079887  0.26623998 0.0000011
## 50K+-500+    0.230586849  0.128532233  0.33264146 0.0000000
## 100K+-500+   0.266641733  0.170408442  0.36287502 0.0000000
## 300K+-500+   0.312421505  0.207839051  0.41700396 0.0000000
## 1M+-500+     0.363172780  0.251123410  0.47522215 0.0000000
## 2.5K+-1K+    0.022541295 -0.075001405  0.12008400 0.9998394
## 5K+-1K+      0.084108986 -0.013490527  0.18170850 0.1727899
## 10K+-1K+     0.114805629  0.026878134  0.20273312 0.0012014
## 25K+-1K+     0.124817930  0.033283243  0.21635262 0.0005180
## 50K+-1K+     0.186744846  0.090454254  0.28303544 0.0000000
## 100K+-1K+    0.222799730  0.132702117  0.31289734 0.0000000
## 300K+-1K+    0.268579502  0.169613735  0.36754527 0.0000000
## 1M+-1K+      0.319330777  0.212504774  0.42615678 0.0000000
## 5K+-2.5K+    0.061567691 -0.041004546  0.16413993 0.7193424
## 10K+-2.5K+   0.092264334 -0.001152170  0.18568084 0.0565429
## 25K+-2.5K+   0.102276635  0.005457227  0.19909604 0.0276896
## 50K+-2.5K+   0.164203551  0.062875978  0.26553112 0.0000078
## 100K+-2.5K+  0.200258435  0.104796512  0.29572036 0.0000000
## 300K+-2.5K+  0.246038206  0.142165102  0.34991131 0.0000000
## 1M+-2.5K+    0.296789482  0.185401898  0.40817707 0.0000000
## 10K+-5K+     0.030696643 -0.062779181  0.12417247 0.9957463
## 25K+-5K+     0.040708944 -0.056167701  0.13758559 0.9685508
## 50K+-5K+     0.102635860  0.001253596  0.20401812 0.0440982
## 100K+-5K+    0.138690744  0.043170771  0.23421072 0.0001331
## 300K+-5K+    0.184470516  0.080544059  0.28839697 0.0000004
## 1M+-5K+      0.235221791  0.123784453  0.34665913 0.0000000
## 25K+-10K+    0.010012302 -0.077112114  0.09713672 0.9999999
## 50K+-10K+    0.071939217 -0.020169104  0.16404754 0.3070668
## 100K+-10K+   0.107994101  0.022380758  0.19360745 0.0022235
## 300K+-10K+   0.153773873  0.058872409  0.24867534 0.0000078
## 1M+-10K+     0.204525148  0.101453039  0.30759726 0.0000000
## 50K+-25K+    0.061926916 -0.033630908  0.15748474 0.6094814
## 100K+-25K+   0.097981800  0.008667751  0.18729585 0.0175649
## 300K+-25K+   0.143761571  0.045508620  0.24201452 0.0001113
## 1M+-25K+     0.194512847  0.088346871  0.30067882 0.0000001
## 100K+-50K+   0.036054884 -0.058127272  0.13023704 0.9846717
## 300K+-50K+   0.081834656 -0.020863551  0.18453286 0.2768896
## 1M+-50K+     0.132585931  0.022293168  0.24287869 0.0048805
## 300K+-100K+  0.045779772 -0.051135776  0.14269532 0.9282456
## 1M+-100K+    0.096531047 -0.008398431  0.20146052 0.1064662
## 1M+-300K+    0.050751275 -0.061884591  0.16338714 0.9479902
# Convert the result to a data frame
tukey_df <- as.data.frame(tukey_result$`as.factor(Review_Category)`)

# Filter for significant p-values
significant_tukey <- tukey_df[tukey_df[4] < 0.05, ]

# Display the significant results
print(significant_tukey)
##                    diff          lwr         upr        p adj
## 100+-0+     -0.09668322 -0.152307271 -0.04105916 8.987756e-07
## 10K+-0+      0.09561480  0.030638525  0.16059107 9.732720e-05
## 25K+-0+      0.10562710  0.035846939  0.17540726 4.884843e-05
## 50K+-0+      0.16755401  0.091642554  0.24346547 0.000000e+00
## 100K+-0+     0.20360890  0.135724795  0.27149300 0.000000e+00
## 300K+-0+     0.24938867  0.170111342  0.32866600 0.000000e+00
## 1M+-0+       0.30013994  0.211244127  0.38903576 0.000000e+00
## 2.5K+-100+   0.10003368  0.012862795  0.18720456 9.667490e-03
## 5K+-100+     0.16160137  0.074366918  0.24883582 9.538328e-08
## 10K+-100+    0.19229801  0.116039053  0.26855697 0.000000e+00
## 25K+-100+    0.20231031  0.121918874  0.28270175 0.000000e+00
## 50K+-100+    0.26423723  0.178469737  0.35000472 0.000000e+00
## 100K+-100+   0.30029211  0.221540831  0.37904339 0.000000e+00
## 300K+-100+   0.34607188  0.257311491  0.43483228 0.000000e+00
## 1M+-100+     0.39682316  0.299375844  0.49427048 0.000000e+00
## 5K+-500+     0.12795099  0.024660470  0.23124151 3.018884e-03
## 10K+-500+    0.15864763  0.064443010  0.25285225 2.473396e-06
## 25K+-500+    0.16865993  0.071079887  0.26623998 1.080775e-06
## 50K+-500+    0.23058685  0.128532233  0.33264146 0.000000e+00
## 100K+-500+   0.26664173  0.170408442  0.36287502 0.000000e+00
## 300K+-500+   0.31242150  0.207839051  0.41700396 0.000000e+00
## 1M+-500+     0.36317278  0.251123410  0.47522215 0.000000e+00
## 10K+-1K+     0.11480563  0.026878134  0.20273312 1.201416e-03
## 25K+-1K+     0.12481793  0.033283243  0.21635262 5.179950e-04
## 50K+-1K+     0.18674485  0.090454254  0.28303544 1.572425e-08
## 100K+-1K+    0.22279973  0.132702117  0.31289734 0.000000e+00
## 300K+-1K+    0.26857950  0.169613735  0.36754527 0.000000e+00
## 1M+-1K+      0.31933078  0.212504774  0.42615678 0.000000e+00
## 25K+-2.5K+   0.10227664  0.005457227  0.19909604 2.768961e-02
## 50K+-2.5K+   0.16420355  0.062875978  0.26553112 7.808701e-06
## 100K+-2.5K+  0.20025843  0.104796512  0.29572036 3.507881e-10
## 300K+-2.5K+  0.24603821  0.142165102  0.34991131 0.000000e+00
## 1M+-2.5K+    0.29678948  0.185401898  0.40817707 0.000000e+00
## 50K+-5K+     0.10263586  0.001253596  0.20401812 4.409823e-02
## 100K+-5K+    0.13869074  0.043170771  0.23421072 1.331239e-04
## 300K+-5K+    0.18447052  0.080544059  0.28839697 4.428778e-07
## 1M+-5K+      0.23522179  0.123784453  0.34665913 2.244944e-10
## 100K+-10K+   0.10799410  0.022380758  0.19360745 2.223466e-03
## 300K+-10K+   0.15377387  0.058872409  0.24867534 7.832139e-06
## 1M+-10K+     0.20452515  0.101453039  0.30759726 5.942656e-09
## 100K+-25K+   0.09798180  0.008667751  0.18729585 1.756493e-02
## 300K+-25K+   0.14376157  0.045508620  0.24201452 1.113055e-04
## 1M+-25K+     0.19451285  0.088346871  0.30067882 1.436204e-07
## 1M+-50K+     0.13258593  0.022293168  0.24287869 4.880458e-03

As we can see, the significant difference for average rating for different review categories is between 0+ and 1M+ as expected.

For easier Ratings and Reviews vs Installs we can group Installs into categories given

ANOVA test for Content Rating vs Installs

# 1. Encode content rating (e.g., as factor levels or one-hot encoding)
data_updated$Content.Rating <- as.factor(data_updated$Content.Rating)

data_updated <- data_updated %>%
  filter(!is.na(Installs) & Installs > 0)

# ANOVA test for difference in installs between content ratings
install_anova <- aov(log10(Installs) ~ Content.Rating, data = data_updated)

print("\nANOVA test results for Installs by Content Rating:")
## [1] "\nANOVA test results for Installs by Content Rating:"
print(summary(install_anova))
##                  Df Sum Sq Mean Sq F value Pr(>F)    
## Content.Rating    5    743  148.57   41.91 <2e-16 ***
## Residuals      9636  34157    3.54                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANOVA analysis : Revealed significant differences in install counts based on content rating (F(5, 9638) = 41.95, p < 2e-16). This indicates that various content ratings have a substantial impact on the number of installs, highlighting the importance of content quality and type in attracting users.

Chi-square Test for Content Rating vs Last Updated

data_updated <- data_final %>%
  mutate(
    update_year = year(Last.Updated),
    update_month = month(Last.Updated),
    update_quarter = quarter(Last.Updated),
    days_since_update = as.numeric(difftime(max(Last.Updated), Last.Updated, units = "days"))
  )

# Monthly update pattern
monthly_updates <- data_updated %>%
  group_by(update_year, update_month) %>%
  summarize(count = n()) %>%
  mutate(date = as.Date(paste(update_year, update_month, "01", sep = "-")))

filtered_data <- data_updated %>%
  filter(!is.na(Content.Rating) & !is.na(update_quarter))

# Step 2: Create the contingency table
contingency_table <- table(filtered_data$Content.Rating, filtered_data$update_quarter)

# Step 3: Perform the Chi-square test
chi_test <- chisq.test(contingency_table)

# Step 4: Print the results
cat("\nChi-square test for independence between Content Rating and Update Quarter:\n")
## 
## Chi-square test for independence between Content Rating and Update Quarter:
print(chi_test)
## 
##  Pearson's Chi-squared test
## 
## data:  contingency_table
## X-squared = 88.096, df = 15, p-value = 2.244e-12

The P value is small signifying that there is statistically significant relationship between Content Rating and Last Updated quarter

Correlation

Correlation for all variables in data_final

Lets convert all the categorical variables into factors and then convert into numerical dataframe for calucalting the correlation matrix

# Step 1: Create a copy of the original data without specific columns
columns_to_remove <- c("App", "Scaled_Reviews", "update_year", "update_month", 
                      "update_quarter", "days_since_update", "week_of_year", 
                      "Last.Updated", "day_of_week", "month_of_year", "season")
data_numeric_or_factor <- data_updated %>%
  select(-any_of(columns_to_remove))  # Changed to any_of to handle missing columns gracefully

# Step 2: Identify and convert character columns to factors
data_numeric_or_factor <- data_numeric_or_factor %>%
  mutate(across(where(is.character), as.factor))

# Step 3: Create a copy for factor data
data_factor <- data_numeric_or_factor

# Step 4: Identify numeric and factor columns
numeric_columns <- sapply(data_numeric_or_factor, is.numeric)
factor_columns <- sapply(data_numeric_or_factor, is.factor)

# Step 5: Convert factors to numeric while preserving numeric columns
data_final_numeric <- data_numeric_or_factor %>%
  mutate(across(where(is.factor), ~as.numeric(as.factor(.))))

# Step 6: Check for any non-numeric columns and remove them
non_numeric_cols <- names(data_final_numeric)[!sapply(data_final_numeric, is.numeric)]
if(length(non_numeric_cols) > 0) {
  data_final_numeric <- data_final_numeric %>%
    select(-all_of(non_numeric_cols))
}

# Step 7: Calculate correlations
# Pearson correlation
pearson_correlation <- cor(data_final_numeric, 
                         method = "pearson", 
                         use = "complete.obs")

# Spearman correlation
spearman_correlation <- cor(data_final_numeric, 
                          method = "spearman", 
                          use = "complete.obs")

# Step 8: Identify strong correlations (added this step)
# Create a matrix of TRUE/FALSE values where correlations are strong
strong_correlation_matrix <- abs(pearson_correlation) >= 0.5 & 
                           upper.tri(pearson_correlation, diag = FALSE)

# Get the indices of strong correlations
strong_correlations <- which(strong_correlation_matrix, arr.ind = TRUE)

# Step 9: Create enhanced correlation plots
# Pearson correlation plot
par(mfrow = c(1, 2))
corrplot(pearson_correlation,
         method = "color",
         type = "upper",
         order = "hclust",
         addCoef.col = "black",
         tl.col = "black",
         tl.srt = 45,
         number.cex = 0.7,
         title = "Pearson Correlation Matrix",
         mar = c(0,0,1,0))

# Spearman correlation plot
corrplot(spearman_correlation,
         method = "color",
         type = "upper",
         order = "hclust",
         addCoef.col = "black",
         tl.col = "black",
         tl.srt = 45,
         number.cex = 0.7,
         title = "Spearman Correlation Matrix",
         mar = c(0,0,1,0))

# Reset plot parameters
par(mfrow = c(1, 1))

# Step 10: Create correlation summary if strong correlations exist
if(nrow(strong_correlations) > 0) {
  correlation_summary <- data.frame(
    Variable1 = rownames(pearson_correlation)[strong_correlations[,1]],
    Variable2 = colnames(pearson_correlation)[strong_correlations[,2]],
    Pearson_Correlation = pearson_correlation[strong_correlations],
    Spearman_Correlation = spearman_correlation[strong_correlations]
  ) %>%
    arrange(desc(abs(Pearson_Correlation)))
  
  print(correlation_summary)
}
##   Variable1 Variable2 Pearson_Correlation Spearman_Correlation
## 1   Reviews  Installs           0.6251632            0.9677196

As seen installs has the highest correlation with the reviews.

As we can see from the both pearson and spearman have relatively different correlation matrices and plots. We can refer to the categorical variables correlation from the spearman.

Correlation Reviews

# Calculate correlation matrices for both factor and numeric data
# For factor data
correlation_matrix <- cor(data_final_numeric, 
                        method = "pearson",
                        use = "complete.obs")

# For numeric data
correlation_matrix1 <- cor(data_numeric_or_factor %>% 
                           select(where(is.numeric)),
                         method = "pearson",
                         use = "complete.obs")

# Extract correlations with Reviews
reviews_correlation_factor <- correlation_matrix[, "Reviews", drop = FALSE]
reviews_correlation_factor1 <- correlation_matrix1[, "Reviews", drop = FALSE]

# Print the correlation matrix for Reviews from factor data
print("Correlations with Reviews (Factor Data):")
## [1] "Correlations with Reviews (Factor Data):"
print(reviews_correlation_factor)
##                     Reviews
## Category        0.017318803
## Rating          0.055027689
## Reviews         1.000000000
## Size            0.075511392
## Installs        0.625163244
## Price          -0.007598276
## Content.Rating  0.055611354
## Android.Ver     0.106433744
# Print the correlation matrix for Reviews from numeric data
print("\nCorrelations with Reviews (Numeric Data):")
## [1] "\nCorrelations with Reviews (Numeric Data):"
print(reviews_correlation_factor1)
##               Reviews
## Rating    0.055027689
## Reviews   1.000000000
## Size      0.075511392
## Installs  0.625163244
## Price    -0.007598276
# Create correlation plots
# For factor data
par(mfrow = c(1, 2))
corrplot(reviews_correlation_factor, 
         method = "color", 
         addCoef.col = "black", 
         title = "Correlation of Reviews\nwith Other Variables (Factor Data)", 
         tl.col = "black", 
         tl.srt = 45,
         mar = c(0,0,2,0))

# For numeric data
corrplot(reviews_correlation_factor1, 
         method = "color", 
         addCoef.col = "black", 
         title = "Correlation of Reviews\nwith Other Variables (Numeric Data)", 
         tl.col = "black", 
         tl.srt = 45,
         mar = c(0,0,2,0))

# Reset plot parameters
par(mfrow = c(1, 1))

As seen reviews has the highest correlation(positive) with the installs and then in spearman correlation matrix it has high correlation(positive) with content rating and android version meaning

Correlation with Rating

rating_correlation_factor <- correlation_matrix[, "Rating", drop = FALSE]

rating_correlation_factor1 <- correlation_matrix1[, "Rating", drop = FALSE]

# Print the correlation matrix for Reviews from numeric factor data
print(rating_correlation_factor)
##                     Rating
## Category       -0.03757526
## Rating          1.00000000
## Reviews         0.05502769
## Size            0.05633562
## Installs        0.04008657
## Price          -0.01953139
## Content.Rating  0.02596523
## Android.Ver     0.05783188
# Step 6: Create a correlation plot for Reviews in data_numeric_or_factor
corrplot(rating_correlation_factor, method = "color", addCoef.col = "black", 
         title = "Correlation of Reviews with Other Variables (Factor Data)", 
         tl.col = "black", tl.srt = 45)

corrplot(rating_correlation_factor1, method = "color", addCoef.col = "black", 
         title = "Correlation of Reviews with Other Variables (Factor Data)", 
         tl.col = "black", tl.srt = 45)

Rating is not much correlated with any of the variables, only slightly positively correlated with reviews and installs which was also demonstrated through visualisation previously.

Correlation with Price

# Spearman correlation for Price
price_correlation_factor1 <- correlation_matrix1[, "Price", drop = FALSE]
print("Spearman Correlation of Price with Other Variables:")
## [1] "Spearman Correlation of Price with Other Variables:"
print(price_correlation_factor1)
##                 Price
## Rating   -0.019531386
## Reviews  -0.007598276
## Size     -0.021577776
## Installs -0.009405822
## Price     1.000000000
# Plot for Spearman correlation with Price
corrplot(price_correlation_factor1, method = "color", addCoef.col = "black", 
         title = "Correlation of Price with Other Variables (Spearman)", 
         tl.col = "black", tl.srt = 45)

Price vs. Log_Installs: -0.06, suggesting a very weak negative relationship between price and the number of installs.

Correlation with Install Vs Price

# Load necessary libraries
library(ggplot2)
library(reshape2)

# Calculate the correlation matrix for Size and Installs
cor_matrix <- cor(data_final[, c("Size", "Installs")], use = "complete.obs", method = "pearson")

# Melt the correlation matrix for easy plotting with ggplot2
melted_cor_matrix <- melt(cor_matrix)

# Plot the heatmap
ggplot(data = melted_cor_matrix, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1,1), space = "Lab", 
                       name="Pearson\nCorrelation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 12, hjust = 1)) +
  ggtitle("Correlation Heatmap: Size vs Installs") +
  labs(x = "Variables", y = "Variables")

Correlation between Install Vs Size

# Calculate Pearson correlation and perform the test
cor_test <- cor.test(data_final$Size, data_final$Installs, method = "pearson")

# Output the correlation coefficient and p-value
cor_test
## 
##  Pearson's product-moment correlation
## 
## data:  data_final$Size and data_final$Installs
## t = 4.0047, df = 9655, p-value = 6.255e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02079459 0.06061872
## sample estimates:
##        cor 
## 0.04072282

According to the relational hypothesis testing: 1. Correlation Coefficient (cor):Pearson correlation coefficient is 0.0407. This indicates a very weak positive relationship between Size and Installs—meaning that as app size increases, installs slightly tend to increase as well, but the effect is minimal.

P-value): The p-value is 6.198e-05 (or 0.00006198), which is much smaller than the conventional significance level (e.g., 0.05). This low p-value means that we can reject the null hypothesis (that there is no correlation) and conclude that x and y are not independent.

Confidence Interval: The 95% confidence interval for the correlation coefficient is between 0.0208 and 0.0606. This range is quite narrow and close to zero, further confirming that while the relationship is significant, the strength of the correlation is very low.

Correlation between Lastupdated vs Installs

# Create a new data frame with relevant variables for correlation analysis
correlation_data <- data_analysis %>%
  select(days_since_update, update_year, update_month) %>%
  mutate(log_installs = log10(data_final$Installs))

# Calculate the correlation matrix
correlation_matrix <- cor(correlation_data, method = "spearman", use = "complete.obs")

# Print the correlation matrix
print("Spearman Correlation Matrix:")
## [1] "Spearman Correlation Matrix:"
corrplot(correlation_matrix, method = "color",
          col = colorRampPalette(c("red", "white", "blue"))(200),
          type = "upper",
          tl.col = "black", tl.srt = 45,
          addCoef.col = "black", # Add correlation coefficients
          number.cex = 0.7,      # Adjust size of numbers
          title = "Correlation Matrix", # Title
          mar = c(0, 0, 1, 0))   # Margins

Correlation Analysis: A moderate negative correlation :(ρ=−0.3317) was found between the number of days since the last update and the log-transformed installs. This indicates that as the time since the last update increases, the number of installs tends to decrease. The relationship is statistically significant (p < 2.2e-16), suggesting that timely updates may be crucial for maintaining user engagement.

Implications These findings suggest that regular updates are important for sustaining app installs, and that different content ratings can influence user engagement. Strategies aimed at timely updates and optimizing content ratings could enhance app performance and user acquisition.